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| <div class="section" id="module-apache_beam.dataframe.frames"> |
| <span id="apache-beam-dataframe-frames-module"></span><h1>apache_beam.dataframe.frames module<a class="headerlink" href="#module-apache_beam.dataframe.frames" title="Permalink to this headline">¶</a></h1> |
| <p>Analogs for <a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.3.0.dev0+1745.g9f65984a9c)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> and <a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v1.3.0.dev0+1745.g9f65984a9c)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a>: |
| <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeferredDataFrame</span></code></a> and <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeferredSeries</span></code></a>.</p> |
| <p>These classes are effectively wrappers around a <a class="reference external" href="https://beam.apache.org/documentation/programming-guide/#what-is-a-schema">schema-aware</a> |
| <a class="reference internal" href="apache_beam.pvalue.html#apache_beam.pvalue.PCollection" title="apache_beam.pvalue.PCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">PCollection</span></code></a> that provide a set of operations |
| compatible with the <a class="reference external" href="https://pandas.pydata.org/">pandas</a> API.</p> |
| <p>Note that we aim for the Beam DataFrame API to be completely compatible with |
| the pandas API, but there are some features that are currently unimplemented |
| for various reasons. Pay particular attention to the <strong>‘Differences from |
| pandas’</strong> section for each operation to understand where we diverge.</p> |
| <dl class="class"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries"> |
| <em class="property">class </em><code class="descclassname">apache_beam.dataframe.frames.</code><code class="descname">DeferredSeries</code><span class="sig-paren">(</span><em>expr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.dataframe.frames.DeferredDataFrameOrSeries</span></code></p> |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.name"> |
| <code class="descname">name</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.name" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.dtype"> |
| <code class="descname">dtype</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.dtype" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.dtypes"> |
| <code class="descname">dtypes</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.dtypes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.keys"> |
| <code class="descname">keys</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.keys"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.keys" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.append"> |
| <code class="descname">append</code><span class="sig-paren">(</span><em>to_append</em>, <em>ignore_index</em>, <em>verify_integrity</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.append"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.append" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.align"> |
| <code class="descname">align</code><span class="sig-paren">(</span><em>other</em>, <em>join</em>, <em>axis</em>, <em>level</em>, <em>method</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.align"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.align" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Align two objects on their axes with the specified join method.</p> |
| <p>Join method is specified for each axis Index.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a><em> or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a>) – </li> |
| <li><strong>join</strong> (<em>{'outer'</em><em>, </em><em>'inner'</em><em>, </em><em>'left'</em><em>, </em><em>'right'}</em><em>, </em><em>default 'outer'</em>) – </li> |
| <li><strong>axis</strong> (<em>allowed axis of the other object</em><em>, </em><em>default None</em>) – Align on index (0), columns (1), or both (None).</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>level name</em><em>, </em><em>default None</em>) – Broadcast across a level, matching Index values on the |
| passed MultiIndex level.</li> |
| <li><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Always returns new objects. If copy=False and no reindexing is |
| required then original objects are returned.</li> |
| <li><strong>fill_value</strong> (<em>scalar</em><em>, </em><em>default np.NaN</em>) – Value to use for missing values. Defaults to NaN, but can be any |
| “compatible” value.</li> |
| <li><strong>method</strong> (<em>{'backfill'</em><em>, </em><em>'bfill'</em><em>, </em><em>'pad'</em><em>, </em><em>'ffill'</em><em>, </em><em>None}</em><em>, </em><em>default None</em>) – <p>Method to use for filling holes in reindexed DeferredSeries:</p> |
| <ul> |
| <li>pad / ffill: propagate last valid observation forward to next valid.</li> |
| <li>backfill / bfill: use NEXT valid observation to fill gap.</li> |
| </ul> |
| </li> |
| <li><strong>limit</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>default None</em>) – If method is specified, this is the maximum number of consecutive |
| NaN values to forward/backward fill. In other words, if there is |
| a gap with more than this number of consecutive NaNs, it will only |
| be partially filled. If method is not specified, this is the |
| maximum number of entries along the entire axis where NaNs will be |
| filled. Must be greater than 0 if not None.</li> |
| <li><strong>fill_axis</strong> (<em>{0</em><em> or </em><em>'index'}</em><em>, </em><em>default 0</em>) – Filling axis, method and limit.</li> |
| <li><strong>broadcast_axis</strong> (<em>{0</em><em> or </em><em>'index'}</em><em>, </em><em>default None</em>) – Broadcast values along this axis, if aligning two objects of |
| different dimensions.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>(left, right)</strong> – Aligned objects.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">(<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a>, type of other)</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>Aligning per-level is not yet supported. Only the default, |
| <code class="docutils literal notranslate"><span class="pre">level=None</span></code>, is allowed.</p> |
| <p>Filling NaN values via <code class="docutils literal notranslate"><span class="pre">method</span></code> is not supported, because it is |
| sensitive to the order of the data |
| (see <a class="reference external" href="https://s.apache.org/dataframe-order-sensitive-operations">https://s.apache.org/dataframe-order-sensitive-operations</a>). Only the |
| default, <code class="docutils literal notranslate"><span class="pre">method=None</span></code>, is allowed.</p> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.array"> |
| <code class="descname">array</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.array" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.array is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.ravel"> |
| <code class="descname">ravel</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.ravel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.ravel is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rename"> |
| <code class="descname">rename</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rename" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Alter Series index labels or name.</p> |
| <p>Function / dict values must be unique (1-to-1). Labels not contained in |
| a dict / Series will be left as-is. Extra labels listed don’t throw an |
| error.</p> |
| <p>Alternatively, change <code class="docutils literal notranslate"><span class="pre">Series.name</span></code> with a scalar value.</p> |
| <p>See the <a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/user_guide/basics.html#basics-rename" title="(in pandas v1.3.0.dev0+1745.g9f65984a9c)"><span class="xref std std-ref">user guide</span></a> for more.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>"index"}</em>) – Unused. Accepted for compatibility with DeferredDataFrame method only.</li> |
| <li><strong>index</strong> (<em>scalar</em><em>, </em><em>hashable sequence</em><em>, </em><em>dict-like</em><em> or </em><em>function</em><em>, </em><em>optional</em>) – Functions or dict-like are transformations to apply to |
| the index. |
| Scalar or hashable sequence-like will alter the <code class="docutils literal notranslate"><span class="pre">DeferredSeries.name</span></code> |
| attribute.</li> |
| <li><strong>**kwargs</strong> – Additional keyword arguments passed to the function. Only the |
| “inplace” keyword is used.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">DeferredSeries with index labels or name altered or None if <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code>.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)">None</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.rename" title="apache_beam.dataframe.frames.DeferredDataFrame.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.rename()</span></code></a></dt> |
| <dd>Corresponding DeferredDataFrame method.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.rename_axis" title="apache_beam.dataframe.frames.DeferredSeries.rename_axis"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.rename_axis()</span></code></a></dt> |
| <dd>Set the name of the axis.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 3</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s2">"my_name"</span><span class="p">)</span> <span class="c1"># scalar, changes Series.name</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 3</span> |
| <span class="go">Name: my_name, dtype: int64</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># function, changes labels</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">4 3</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">rename</span><span class="p">({</span><span class="mi">1</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mi">5</span><span class="p">})</span> <span class="c1"># mapping, changes labels</span> |
| <span class="go">0 1</span> |
| <span class="go">3 2</span> |
| <span class="go">5 3</span> |
| <span class="go">dtype: int64</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.between"> |
| <code class="descname">between</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.between" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return boolean Series equivalent to left <= series <= right.</p> |
| <p>This function returns a boolean vector containing <cite>True</cite> wherever the |
| corresponding Series element is between the boundary values <cite>left</cite> and |
| <cite>right</cite>. NA values are treated as <cite>False</cite>.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>left</strong> (<em>scalar</em><em> or </em><em>list-like</em>) – Left boundary.</li> |
| <li><strong>right</strong> (<em>scalar</em><em> or </em><em>list-like</em>) – Right boundary.</li> |
| <li><strong>inclusive</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Include boundaries.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">DeferredSeries representing whether each element is between left and |
| right (inclusive).</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.gt" title="apache_beam.dataframe.frames.DeferredSeries.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.gt()</span></code></a></dt> |
| <dd>Greater than of series and other.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.lt" title="apache_beam.dataframe.frames.DeferredSeries.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.lt()</span></code></a></dt> |
| <dd>Less than of series and other.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>This function is equivalent to <code class="docutils literal notranslate"><span class="pre">(left</span> <span class="pre"><=</span> <span class="pre">ser)</span> <span class="pre">&</span> <span class="pre">(ser</span> <span class="pre"><=</span> <span class="pre">right)</span></code></p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">])</span> |
| |
| <span class="go">Boundary values are included by default:</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> |
| <span class="go">0 True</span> |
| <span class="go">1 False</span> |
| <span class="go">2 True</span> |
| <span class="go">3 False</span> |
| <span class="go">4 False</span> |
| <span class="go">dtype: bool</span> |
| |
| <span class="go">With `inclusive` set to ``False`` boundary values are excluded:</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">inclusive</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="go">0 True</span> |
| <span class="go">1 False</span> |
| <span class="go">2 False</span> |
| <span class="go">3 False</span> |
| <span class="go">4 False</span> |
| <span class="go">dtype: bool</span> |
| |
| <span class="go">`left` and `right` can be any scalar value:</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">'Alice'</span><span class="p">,</span> <span class="s1">'Bob'</span><span class="p">,</span> <span class="s1">'Carol'</span><span class="p">,</span> <span class="s1">'Eve'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="s1">'Anna'</span><span class="p">,</span> <span class="s1">'Daniel'</span><span class="p">)</span> |
| <span class="go">0 False</span> |
| <span class="go">1 True</span> |
| <span class="go">2 True</span> |
| <span class="go">3 False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.add_suffix"> |
| <code class="descname">add_suffix</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.add_suffix" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Suffix labels with string <cite>suffix</cite>.</p> |
| <p>For Series, the row labels are suffixed. |
| For DataFrame, the column labels are suffixed.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>suffix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – The string to add after each label.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">New DeferredSeries or DeferredDataFrame with updated labels.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.add_prefix" title="apache_beam.dataframe.frames.DeferredSeries.add_prefix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.add_prefix()</span></code></a></dt> |
| <dd>Prefix row labels with string <cite>prefix</cite>.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.add_prefix" title="apache_beam.dataframe.frames.DeferredDataFrame.add_prefix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.add_prefix()</span></code></a></dt> |
| <dd>Prefix column labels with string <cite>prefix</cite>.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 3</span> |
| <span class="go">3 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">add_suffix</span><span class="p">(</span><span class="s1">'_item'</span><span class="p">)</span> |
| <span class="go">0_item 1</span> |
| <span class="go">1_item 2</span> |
| <span class="go">2_item 3</span> |
| <span class="go">3_item 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">add_suffix</span><span class="p">(</span><span class="s1">'_col'</span><span class="p">)</span> |
| <span class="go"> A_col B_col</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.add_prefix"> |
| <code class="descname">add_prefix</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.add_prefix" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Prefix labels with string <cite>prefix</cite>.</p> |
| <p>For Series, the row labels are prefixed. |
| For DataFrame, the column labels are prefixed.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>prefix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – The string to add before each label.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">New DeferredSeries or DeferredDataFrame with updated labels.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.add_suffix" title="apache_beam.dataframe.frames.DeferredSeries.add_suffix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.add_suffix()</span></code></a></dt> |
| <dd>Suffix row labels with string <cite>suffix</cite>.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.add_suffix" title="apache_beam.dataframe.frames.DeferredDataFrame.add_suffix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.add_suffix()</span></code></a></dt> |
| <dd>Suffix column labels with string <cite>suffix</cite>.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 3</span> |
| <span class="go">3 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s1">'item_'</span><span class="p">)</span> |
| <span class="go">item_0 1</span> |
| <span class="go">item_1 2</span> |
| <span class="go">item_2 3</span> |
| <span class="go">item_3 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s1">'col_'</span><span class="p">)</span> |
| <span class="go"> col_A col_B</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.dot"> |
| <code class="descname">dot</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.dot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.dot" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.std"> |
| <code class="descname">std</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.std"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.std" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return sample standard deviation over requested axis.</p> |
| <p>Normalized by N-1 by default. This can be changed using the ddof argument</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>axis</strong> (<em>{index</em><em> (</em><em>0</em><em>)</em><em>}</em>) – </li> |
| <li><strong>skipna</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Exclude NA/null values. If an entire row/column is NA, the result |
| will be NA.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>level name</em><em>, </em><em>default None</em>) – If the axis is a MultiIndex (hierarchical), count along a |
| particular level, collapsing into a scalar.</li> |
| <li><strong>ddof</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>default 1</em>) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, |
| where N represents the number of elements.</li> |
| <li><strong>numeric_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default None</em>) – Include only float, int, boolean columns. If None, will attempt to use |
| everything, then use only numeric data. Not implemented for DeferredSeries.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">scalar or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> (if level specified)</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Notes</p> |
| <p>To have the same behaviour as <cite>numpy.std</cite>, use <cite>ddof=0</cite> (instead of the |
| default <cite>ddof=1</cite>)</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.var"> |
| <code class="descname">var</code><span class="sig-paren">(</span><em>axis</em>, <em>skipna</em>, <em>level</em>, <em>ddof</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.var"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.var" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return unbiased variance over requested axis.</p> |
| <p>Normalized by N-1 by default. This can be changed using the ddof argument</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>axis</strong> (<em>{index</em><em> (</em><em>0</em><em>)</em><em>}</em>) – </li> |
| <li><strong>skipna</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Exclude NA/null values. If an entire row/column is NA, the result |
| will be NA.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>level name</em><em>, </em><em>default None</em>) – If the axis is a MultiIndex (hierarchical), count along a |
| particular level, collapsing into a scalar.</li> |
| <li><strong>ddof</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>default 1</em>) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, |
| where N represents the number of elements.</li> |
| <li><strong>numeric_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default None</em>) – Include only float, int, boolean columns. If None, will attempt to use |
| everything, then use only numeric data. Not implemented for DeferredSeries.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">scalar or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> (if level specified)</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>Per-level aggregation is not yet supported (BEAM-11777). Only the |
| default, <code class="docutils literal notranslate"><span class="pre">level=None</span></code>, is allowed.</p> |
| <p class="rubric">Notes</p> |
| <p>To have the same behaviour as <cite>numpy.std</cite>, use <cite>ddof=0</cite> (instead of the |
| default <cite>ddof=1</cite>)</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.corr"> |
| <code class="descname">corr</code><span class="sig-paren">(</span><em>other</em>, <em>method</em>, <em>min_periods</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.corr"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.corr" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.cov"> |
| <code class="descname">cov</code><span class="sig-paren">(</span><em>other</em>, <em>min_periods</em>, <em>ddof</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.cov"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.cov" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.dropna"> |
| <code class="descname">dropna</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.dropna"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.dropna" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.isnull"> |
| <code class="descname">isnull</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.isnull" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect missing values.</p> |
| <p>Return a boolean same-sized object indicating if the values are NA. |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, gets mapped to True |
| values. |
| Everything else gets mapped to False values. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>).</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredSeries that |
| indicates whether an element is an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isnull" title="apache_beam.dataframe.frames.DeferredSeries.isnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.isnull()</span></code></a></dt> |
| <dd>Alias of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.notna" title="apache_beam.dataframe.frames.DeferredSeries.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.notna()</span></code></a></dt> |
| <dd>Boolean inverse of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.dropna" title="apache_beam.dataframe.frames.DeferredSeries.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isna" title="apache_beam.dataframe.frames.DeferredSeries.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">isna()</span></code></a></dt> |
| <dd>Top-level isna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 False True False True</span> |
| <span class="go">1 False False False False</span> |
| <span class="go">2 True False False False</span> |
| |
| <span class="go">Show which entries in a Series are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go">0 False</span> |
| <span class="go">1 False</span> |
| <span class="go">2 True</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.isna"> |
| <code class="descname">isna</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.isna" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect missing values.</p> |
| <p>Return a boolean same-sized object indicating if the values are NA. |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, gets mapped to True |
| values. |
| Everything else gets mapped to False values. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>).</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredSeries that |
| indicates whether an element is an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isnull" title="apache_beam.dataframe.frames.DeferredSeries.isnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.isnull()</span></code></a></dt> |
| <dd>Alias of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.notna" title="apache_beam.dataframe.frames.DeferredSeries.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.notna()</span></code></a></dt> |
| <dd>Boolean inverse of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.dropna" title="apache_beam.dataframe.frames.DeferredSeries.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isna" title="apache_beam.dataframe.frames.DeferredSeries.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">isna()</span></code></a></dt> |
| <dd>Top-level isna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 False True False True</span> |
| <span class="go">1 False False False False</span> |
| <span class="go">2 True False False False</span> |
| |
| <span class="go">Show which entries in a Series are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go">0 False</span> |
| <span class="go">1 False</span> |
| <span class="go">2 True</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.notnull"> |
| <code class="descname">notnull</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.notnull" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect existing (non-missing) values.</p> |
| <p>Return a boolean same-sized object indicating if the values are not NA. |
| Non-missing values get mapped to True. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>). |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, get mapped to False |
| values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredSeries that |
| indicates whether an element is not an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.notnull" title="apache_beam.dataframe.frames.DeferredSeries.notnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.notnull()</span></code></a></dt> |
| <dd>Alias of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isna" title="apache_beam.dataframe.frames.DeferredSeries.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.isna()</span></code></a></dt> |
| <dd>Boolean inverse of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.dropna" title="apache_beam.dataframe.frames.DeferredSeries.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.notna" title="apache_beam.dataframe.frames.DeferredSeries.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">notna()</span></code></a></dt> |
| <dd>Top-level notna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 True False True False</span> |
| <span class="go">1 True True True True</span> |
| <span class="go">2 False True True True</span> |
| |
| <span class="go">Show which entries in a Series are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go">0 True</span> |
| <span class="go">1 True</span> |
| <span class="go">2 False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.notna"> |
| <code class="descname">notna</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.notna" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect existing (non-missing) values.</p> |
| <p>Return a boolean same-sized object indicating if the values are not NA. |
| Non-missing values get mapped to True. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>). |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, get mapped to False |
| values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredSeries that |
| indicates whether an element is not an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.notnull" title="apache_beam.dataframe.frames.DeferredSeries.notnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.notnull()</span></code></a></dt> |
| <dd>Alias of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isna" title="apache_beam.dataframe.frames.DeferredSeries.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.isna()</span></code></a></dt> |
| <dd>Boolean inverse of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.dropna" title="apache_beam.dataframe.frames.DeferredSeries.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.notna" title="apache_beam.dataframe.frames.DeferredSeries.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">notna()</span></code></a></dt> |
| <dd>Top-level notna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 True False True False</span> |
| <span class="go">1 True True True True</span> |
| <span class="go">2 False True True True</span> |
| |
| <span class="go">Show which entries in a Series are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go">0 True</span> |
| <span class="go">1 True</span> |
| <span class="go">2 False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.items"> |
| <code class="descname">items</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.items" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.items is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.iteritems"> |
| <code class="descname">iteritems</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.iteritems" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.iteritems is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.tolist"> |
| <code class="descname">tolist</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.tolist" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.tolist is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_numpy"> |
| <code class="descname">to_numpy</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_numpy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.to_numpy is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_string"> |
| <code class="descname">to_string</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_string" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.to_string is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.aggregate"> |
| <code class="descname">aggregate</code><span class="sig-paren">(</span><em>func</em>, <em>axis=0</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.aggregate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.aggregate" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.agg"> |
| <code class="descname">agg</code><span class="sig-paren">(</span><em>func</em>, <em>axis=0</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.agg" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.axes"> |
| <code class="descname">axes</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.axes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.clip"> |
| <code class="descname">clip</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.clip" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Trim values at input threshold(s).</p> |
| <p>Assigns values outside boundary to boundary values. Thresholds |
| can be singular values or array like, and in the latter case |
| the clipping is performed element-wise in the specified axis.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>lower</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>array_like</em><em>, </em><em>default None</em>) – Minimum threshold value. All values below this |
| threshold will be set to it.</li> |
| <li><strong>upper</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>array_like</em><em>, </em><em>default None</em>) – Maximum threshold value. All values above this |
| threshold will be set to it.</li> |
| <li><strong>axis</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>str axis name</em><em>, </em><em>optional</em>) – Align object with lower and upper along the given axis.</li> |
| <li><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default False</em>) – Whether to perform the operation in place on the data.</li> |
| <li><strong>**kwargs</strong> (<em>*args</em><em>,</em>) – <p>Additional keywords have no effect but might be accepted |
| for compatibility with numpy.</p> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Same type as calling object with the values outside the |
| clip boundaries replaced or None if <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code>.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a> or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)">None</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.clip" title="apache_beam.dataframe.frames.DeferredSeries.clip"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.clip()</span></code></a></dt> |
| <dd>Trim values at input threshold in series.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.clip" title="apache_beam.dataframe.frames.DeferredDataFrame.clip"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.clip()</span></code></a></dt> |
| <dd>Trim values at input threshold in dataframe.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">numpy.clip()</span></code></dt> |
| <dd>Clip (limit) the values in an array.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'col_0'</span><span class="p">:</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s1">'col_1'</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">]}</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> col_0 col_1</span> |
| <span class="go">0 9 -2</span> |
| <span class="go">1 -3 -7</span> |
| <span class="go">2 0 6</span> |
| <span class="go">3 -1 8</span> |
| <span class="go">4 5 -5</span> |
| |
| <span class="go">Clips per column using lower and upper thresholds:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span> |
| <span class="go"> col_0 col_1</span> |
| <span class="go">0 6 -2</span> |
| <span class="go">1 -3 -4</span> |
| <span class="go">2 0 6</span> |
| <span class="go">3 -1 6</span> |
| <span class="go">4 5 -4</span> |
| |
| <span class="go">Clips using specific lower and upper thresholds per column element:</span> |
| |
| <span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">t</span> |
| <span class="go">0 2</span> |
| <span class="go">1 -4</span> |
| <span class="go">2 -1</span> |
| <span class="go">3 6</span> |
| <span class="go">4 3</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span> <span class="o">+</span> <span class="mi">4</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go"> col_0 col_1</span> |
| <span class="go">0 6 2</span> |
| <span class="go">1 -3 -4</span> |
| <span class="go">2 0 3</span> |
| <span class="go">3 6 8</span> |
| <span class="go">4 5 3</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.all"> |
| <code class="descname">all</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.all" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.any"> |
| <code class="descname">any</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.any" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.count"> |
| <code class="descname">count</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.count" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.min"> |
| <code class="descname">min</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.min" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.max"> |
| <code class="descname">max</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.max" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.prod"> |
| <code class="descname">prod</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.prod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.product"> |
| <code class="descname">product</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.product" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.sum"> |
| <code class="descname">sum</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.sum" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.mean"> |
| <code class="descname">mean</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.mean" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.median"> |
| <code class="descname">median</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.median" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.argmax"> |
| <code class="descname">argmax</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.argmax" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.argmax is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.argmin"> |
| <code class="descname">argmin</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.argmin" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.argmin is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.cummax"> |
| <code class="descname">cummax</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.cummax" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.cummax is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.cummin"> |
| <code class="descname">cummin</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.cummin" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.cummin is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.cumprod"> |
| <code class="descname">cumprod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.cumprod" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.cumprod is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.cumsum"> |
| <code class="descname">cumsum</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.cumsum" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.cumsum is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.diff"> |
| <code class="descname">diff</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.diff" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.diff is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.first"> |
| <code class="descname">first</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.first" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.first is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.head"> |
| <code class="descname">head</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.head" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.head is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.interpolate"> |
| <code class="descname">interpolate</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.interpolate" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.interpolate is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.last"> |
| <code class="descname">last</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.last" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.last is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.searchsorted"> |
| <code class="descname">searchsorted</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.searchsorted" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.searchsorted is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.shift"> |
| <code class="descname">shift</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.shift" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.shift is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.tail"> |
| <code class="descname">tail</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.tail" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.tail is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.filter"> |
| <code class="descname">filter</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.filter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Subset the dataframe rows or columns according to the specified index labels.</p> |
| <p>Note that this routine does not filter a dataframe on its |
| contents. The filter is applied to the labels of the index.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>items</strong> (<em>list-like</em>) – Keep labels from axis which are in items.</li> |
| <li><strong>like</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – Keep labels from axis for which “like in label == True”.</li> |
| <li><strong>regex</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em> (</em><em>regular expression</em><em>)</em>) – Keep labels from axis for which re.search(regex, label) == True.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>‘index’</em><em>, </em><em>1</em><em> or </em><em>‘columns’</em><em>, </em><em>None}</em><em>, </em><em>default None</em>) – The axis to filter on, expressed either as an index (int) |
| or axis name (str). By default this is the info axis, |
| ‘index’ for DeferredSeries, ‘columns’ for DeferredDataFrame.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">same type as input object</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.loc" title="apache_beam.dataframe.frames.DeferredDataFrame.loc"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.loc()</span></code></a></dt> |
| <dd>Access a group of rows and columns by label(s) or a boolean array.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>The <code class="docutils literal notranslate"><span class="pre">items</span></code>, <code class="docutils literal notranslate"><span class="pre">like</span></code>, and <code class="docutils literal notranslate"><span class="pre">regex</span></code> parameters are |
| enforced to be mutually exclusive.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">axis</span></code> defaults to the info axis that is used when indexing |
| with <code class="docutils literal notranslate"><span class="pre">[]</span></code>.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])),</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'mouse'</span><span class="p">,</span> <span class="s1">'rabbit'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'two'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> one two three</span> |
| <span class="go">mouse 1 2 3</span> |
| <span class="go">rabbit 4 5 6</span> |
| |
| <span class="gp">>>> </span><span class="c1"># select columns by name</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">items</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span> |
| <span class="go"> one three</span> |
| <span class="go">mouse 1 3</span> |
| <span class="go">rabbit 4 6</span> |
| |
| <span class="gp">>>> </span><span class="c1"># select columns by regular expression</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">regex</span><span class="o">=</span><span class="s1">'e$'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> one three</span> |
| <span class="go">mouse 1 3</span> |
| <span class="go">rabbit 4 6</span> |
| |
| <span class="gp">>>> </span><span class="c1"># select rows containing 'bbi'</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'bbi'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go"> one two three</span> |
| <span class="go">rabbit 4 5 6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.memory_usage"> |
| <code class="descname">memory_usage</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.memory_usage" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.memory_usage is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.nlargest"> |
| <code class="descname">nlargest</code><span class="sig-paren">(</span><em>keep</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.nlargest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.nlargest" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.nsmallest"> |
| <code class="descname">nsmallest</code><span class="sig-paren">(</span><em>keep</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.nsmallest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.nsmallest" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.is_unique"> |
| <code class="descname">is_unique</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.is_unique" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.plot"> |
| <code class="descname">plot</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.plot" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.plot is not supported in |
| the Beam DataFrame API because it is a plotting tool.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.pop"> |
| <code class="descname">pop</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.pop" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.pop is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rename_axis"> |
| <code class="descname">rename_axis</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rename_axis" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set the name of the axis for the index or columns.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>mapper</strong> (<em>scalar</em><em>, </em><em>list-like</em><em>, </em><em>optional</em>) – Value to set the axis name attribute.</li> |
| <li><strong>columns</strong> (<em>index</em><em>,</em>) – <p>A scalar, list-like, dict-like or functions transformations to |
| apply to that axis’ values. |
| Note that the <code class="docutils literal notranslate"><span class="pre">columns</span></code> parameter is not allowed if the |
| object is a DeferredSeries. This parameter only apply for DeferredDataFrame |
| type objects.</p> |
| <p>Use either <code class="docutils literal notranslate"><span class="pre">mapper</span></code> and <code class="docutils literal notranslate"><span class="pre">axis</span></code> to |
| specify the axis to target with <code class="docutils literal notranslate"><span class="pre">mapper</span></code>, or <code class="docutils literal notranslate"><span class="pre">index</span></code> |
| and/or <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p> |
| <div class="versionchanged"> |
| <p><span class="versionmodified">Changed in version 0.24.0.</span></p> |
| </div> |
| </li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 0</em>) – The axis to rename.</li> |
| <li><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Also copy underlying data.</li> |
| <li><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default False</em>) – Modifies the object directly, instead of creating a new DeferredSeries |
| or DeferredDataFrame.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The same type as the caller or None if <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code>.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a>, <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a>, or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)">None</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.rename" title="apache_beam.dataframe.frames.DeferredSeries.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.rename()</span></code></a></dt> |
| <dd>Alter DeferredSeries index labels or name.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.rename" title="apache_beam.dataframe.frames.DeferredDataFrame.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.rename()</span></code></a></dt> |
| <dd>Alter DeferredDataFrame index labels or name.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">Index.rename()</span></code></dt> |
| <dd>Set new names on index.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p><code class="docutils literal notranslate"><span class="pre">DeferredDataFrame.rename_axis</span></code> supports two calling conventions</p> |
| <ul class="simple"> |
| <li><code class="docutils literal notranslate"><span class="pre">(index=index_mapper,</span> <span class="pre">columns=columns_mapper,</span> <span class="pre">...)</span></code></li> |
| <li><code class="docutils literal notranslate"><span class="pre">(mapper,</span> <span class="pre">axis={'index',</span> <span class="pre">'columns'},</span> <span class="pre">...)</span></code></li> |
| </ul> |
| <p>The first calling convention will only modify the names of |
| the index and/or the names of the Index object that is the columns. |
| In this case, the parameter <code class="docutils literal notranslate"><span class="pre">copy</span></code> is ignored.</p> |
| <p>The second calling convention will modify the names of the |
| corresponding index if mapper is a list or a scalar. |
| However, if mapper is dict-like or a function, it will use the |
| deprecated behavior of modifying the axis <em>labels</em>.</p> |
| <p>We <em>highly</em> recommend using keyword arguments to clarify your |
| intent.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">**Series**</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"dog"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"monkey"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 dog</span> |
| <span class="go">1 cat</span> |
| <span class="go">2 monkey</span> |
| <span class="go">dtype: object</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="s2">"animal"</span><span class="p">)</span> |
| <span class="go">animal</span> |
| <span class="go">0 dog</span> |
| <span class="go">1 cat</span> |
| <span class="go">2 monkey</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">**DataFrame**</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"num_legs"</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s2">"num_arms"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"dog"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"monkey"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> num_legs num_arms</span> |
| <span class="go">dog 4 0</span> |
| <span class="go">cat 4 0</span> |
| <span class="go">monkey 2 2</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="s2">"animal"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> num_legs num_arms</span> |
| <span class="go">animal</span> |
| <span class="go">dog 4 0</span> |
| <span class="go">cat 4 0</span> |
| <span class="go">monkey 2 2</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="s2">"limbs"</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s2">"columns"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go">limbs num_legs num_arms</span> |
| <span class="go">animal</span> |
| <span class="go">dog 4 0</span> |
| <span class="go">cat 4 0</span> |
| <span class="go">monkey 2 2</span> |
| |
| <span class="go">**MultiIndex**</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_product</span><span class="p">([[</span><span class="s1">'mammal'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'dog'</span><span class="p">,</span> <span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'monkey'</span><span class="p">]],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'type'</span><span class="p">,</span> <span class="s1">'name'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go">limbs num_legs num_arms</span> |
| <span class="go">type name</span> |
| <span class="go">mammal dog 4 0</span> |
| <span class="go"> cat 4 0</span> |
| <span class="go"> monkey 2 2</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="s1">'class'</span><span class="p">})</span> |
| <span class="go">limbs num_legs num_arms</span> |
| <span class="go">class name</span> |
| <span class="go">mammal dog 4 0</span> |
| <span class="go"> cat 4 0</span> |
| <span class="go"> monkey 2 2</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="nb">str</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span> |
| <span class="go">LIMBS num_legs num_arms</span> |
| <span class="go">type name</span> |
| <span class="go">mammal dog 4 0</span> |
| <span class="go"> cat 4 0</span> |
| <span class="go"> monkey 2 2</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.replace"> |
| <code class="descname">replace</code><span class="sig-paren">(</span><em>to_replace</em>, <em>value</em>, <em>limit</em>, <em>method</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.replace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.replace" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.round"> |
| <code class="descname">round</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.round" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Round each value in a Series to the given number of decimals.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>decimals</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>default 0</em>) – Number of decimal places to round to. If decimals is negative, |
| it specifies the number of positions to the left of the decimal point.</li> |
| <li><strong>**kwargs</strong> (<em>*args</em><em>,</em>) – <p>Additional arguments and keywords have no effect but might be |
| accepted for compatibility with NumPy.</p> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Rounded values of the DeferredSeries.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">numpy.around()</span></code></dt> |
| <dd>Round values of an np.array.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.round" title="apache_beam.dataframe.frames.DeferredDataFrame.round"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.round()</span></code></a></dt> |
| <dd>Round values of a DeferredDataFrame.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">,</span> <span class="mf">2.7</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">round</span><span class="p">()</span> |
| <span class="go">0 0.0</span> |
| <span class="go">1 1.0</span> |
| <span class="go">2 3.0</span> |
| <span class="go">dtype: float64</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.take"> |
| <code class="descname">take</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.take" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.take is not supported in |
| the Beam DataFrame API because it is deprecated in pandas.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_dict"> |
| <code class="descname">to_dict</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.to_dict is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_frame"> |
| <code class="descname">to_frame</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_frame" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Convert Series to DataFrame.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><em>object</em></a><em>, </em><em>default None</em>) – The passed name should substitute for the series name (if it has |
| one).</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">DeferredDataFrame representation of DeferredSeries.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="s2">"vals"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span> |
| <span class="go"> vals</span> |
| <span class="go">0 a</span> |
| <span class="go">1 b</span> |
| <span class="go">2 c</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.unique"> |
| <code class="descname">unique</code><span class="sig-paren">(</span><em>as_series=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.unique"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.unique" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.update"> |
| <code class="descname">update</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredSeries.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.update" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.unstack"> |
| <code class="descname">unstack</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.unstack" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.unstack is not supported in |
| the Beam DataFrame API because the columns in the output DataFrame depend on the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.values"> |
| <code class="descname">values</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.values" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.values is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.view"> |
| <code class="descname">view</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.view" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.Series.view is not supported in |
| the Beam DataFrame API because it relies on memory-sharing semantics that are not compatible with the Beam model.</p> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.str"> |
| <code class="descname">str</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.str" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.apply"> |
| <code class="descname">apply</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.apply" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Invoke function on values of Series.</p> |
| <p>Can be ufunc (a NumPy function that applies to the entire Series) |
| or a Python function that only works on single values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>func</strong> (<em>function</em>) – Python function or NumPy ufunc to apply.</li> |
| <li><strong>convert_dtype</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Try to find better dtype for elementwise function results. If |
| False, leave as dtype=object.</li> |
| <li><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a>) – Positional arguments passed to func after the series value.</li> |
| <li><strong>**kwds</strong> – Additional keyword arguments passed to func.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">If func returns a DeferredSeries object the result will be a DeferredDataFrame.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.map" title="apache_beam.dataframe.frames.DeferredSeries.map"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.map()</span></code></a></dt> |
| <dd>For element-wise operations.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.agg" title="apache_beam.dataframe.frames.DeferredSeries.agg"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.agg()</span></code></a></dt> |
| <dd>Only perform aggregating type operations.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.transform" title="apache_beam.dataframe.frames.DeferredSeries.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.transform()</span></code></a></dt> |
| <dd>Only perform transforming type operations.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Create a series with typical summer temperatures for each city.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">20</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">12</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'London'</span><span class="p">,</span> <span class="s1">'New York'</span><span class="p">,</span> <span class="s1">'Helsinki'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">London 20</span> |
| <span class="go">New York 21</span> |
| <span class="go">Helsinki 12</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Square the values by defining a function and passing it as an</span> |
| <span class="go">argument to ``apply()``.</span> |
| |
| <span class="gp">>>> </span><span class="k">def</span> <span class="nf">square</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> |
| <span class="gp">... </span> <span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">square</span><span class="p">)</span> |
| <span class="go">London 400</span> |
| <span class="go">New York 441</span> |
| <span class="go">Helsinki 144</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Square the values by passing an anonymous function as an</span> |
| <span class="go">argument to ``apply()``.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">London 400</span> |
| <span class="go">New York 441</span> |
| <span class="go">Helsinki 144</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Define a custom function that needs additional positional</span> |
| <span class="go">arguments and pass these additional arguments using the</span> |
| <span class="go">``args`` keyword.</span> |
| |
| <span class="gp">>>> </span><span class="k">def</span> <span class="nf">subtract_custom_value</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">custom_value</span><span class="p">):</span> |
| <span class="gp">... </span> <span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">custom_value</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">subtract_custom_value</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,))</span> |
| <span class="go">London 15</span> |
| <span class="go">New York 16</span> |
| <span class="go">Helsinki 7</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Define a custom function that takes keyword arguments</span> |
| <span class="go">and pass these arguments to ``apply``.</span> |
| |
| <span class="gp">>>> </span><span class="k">def</span> <span class="nf">add_custom_values</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="gp">... </span> <span class="k">for</span> <span class="n">month</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">x</span> <span class="o">+=</span> <span class="n">kwargs</span><span class="p">[</span><span class="n">month</span><span class="p">]</span> |
| <span class="gp">... </span> <span class="k">return</span> <span class="n">x</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">add_custom_values</span><span class="p">,</span> <span class="n">june</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">july</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">august</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span> |
| <span class="go">London 95</span> |
| <span class="go">New York 96</span> |
| <span class="go">Helsinki 87</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Use a function from the Numpy library.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">)</span> |
| <span class="go">London 2.995732</span> |
| <span class="go">New York 3.044522</span> |
| <span class="go">Helsinki 2.484907</span> |
| <span class="go">dtype: float64</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.map"> |
| <code class="descname">map</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.map" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Map values of Series according to input correspondence.</p> |
| <p>Used for substituting each value in a Series with another value, |
| that may be derived from a function, a <code class="docutils literal notranslate"><span class="pre">dict</span></code> or |
| a <code class="xref py py-class docutils literal notranslate"><span class="pre">Series</span></code>.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>arg</strong> (<em>function</em><em>, </em><em>collections.abc.Mapping subclass</em><em> or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a>) – Mapping correspondence.</li> |
| <li><strong>na_action</strong> (<em>{None</em><em>, </em><em>'ignore'}</em><em>, </em><em>default None</em>) – If ‘ignore’, propagate NaN values, without passing them to the |
| mapping correspondence.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Same index as caller.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.apply" title="apache_beam.dataframe.frames.DeferredSeries.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.apply()</span></code></a></dt> |
| <dd>For applying more complex functions on a DeferredSeries.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.apply" title="apache_beam.dataframe.frames.DeferredDataFrame.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.apply()</span></code></a></dt> |
| <dd>Apply a function row-/column-wise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.applymap" title="apache_beam.dataframe.frames.DeferredDataFrame.applymap"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.applymap()</span></code></a></dt> |
| <dd>Apply a function elementwise on a whole DeferredDataFrame.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>When <code class="docutils literal notranslate"><span class="pre">arg</span></code> is a dictionary, values in DeferredSeries that are not in the |
| dictionary (as keys) are converted to <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. However, if the |
| dictionary is a <code class="docutils literal notranslate"><span class="pre">dict</span></code> subclass that defines <code class="docutils literal notranslate"><span class="pre">__missing__</span></code> (i.e. |
| provides a method for default values), then this default is used |
| rather than <code class="docutils literal notranslate"><span class="pre">NaN</span></code>.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">'rabbit'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 cat</span> |
| <span class="go">1 dog</span> |
| <span class="go">2 NaN</span> |
| <span class="go">3 rabbit</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">``map`` accepts a ``dict`` or a ``Series``. Values that are not found</span> |
| <span class="go">in the ``dict`` are converted to ``NaN``, unless the dict has a default</span> |
| <span class="go">value (e.g. ``defaultdict``):</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">map</span><span class="p">({</span><span class="s1">'cat'</span><span class="p">:</span> <span class="s1">'kitten'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">:</span> <span class="s1">'puppy'</span><span class="p">})</span> |
| <span class="go">0 kitten</span> |
| <span class="go">1 puppy</span> |
| <span class="go">2 NaN</span> |
| <span class="go">3 NaN</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">It also accepts a function:</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="s1">'I am a </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">)</span> |
| <span class="go">0 I am a cat</span> |
| <span class="go">1 I am a dog</span> |
| <span class="go">2 I am a nan</span> |
| <span class="go">3 I am a rabbit</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">To avoid applying the function to missing values (and keep them as</span> |
| <span class="go">``NaN``) ``na_action='ignore'`` can be used:</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="s1">'I am a </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">,</span> <span class="n">na_action</span><span class="o">=</span><span class="s1">'ignore'</span><span class="p">)</span> |
| <span class="go">0 I am a cat</span> |
| <span class="go">1 I am a dog</span> |
| <span class="go">2 NaN</span> |
| <span class="go">3 I am a rabbit</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.T"> |
| <code class="descname">T</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.T" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.abs"> |
| <code class="descname">abs</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.abs" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a Series/DataFrame with absolute numeric value of each element.</p> |
| <p>This function only applies to elements that are all numeric.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">DeferredSeries/DeferredDataFrame containing the absolute value of each element.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">abs</td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">numpy.absolute()</span></code></dt> |
| <dd>Calculate the absolute value element-wise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>For <code class="docutils literal notranslate"><span class="pre">complex</span></code> inputs, <code class="docutils literal notranslate"><span class="pre">1.2</span> <span class="pre">+</span> <span class="pre">1j</span></code>, the absolute value is |
| <span class="math notranslate nohighlight">\(\sqrt{ a^2 + b^2 }\)</span>.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Absolute numeric values in a Series.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="o">-</span><span class="mf">1.10</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.33</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> |
| <span class="go">0 1.10</span> |
| <span class="go">1 2.00</span> |
| <span class="go">2 3.33</span> |
| <span class="go">3 4.00</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="go">Absolute numeric values in a Series with complex numbers.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mf">1.2</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> |
| <span class="go">0 1.56205</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="go">Absolute numeric values in a Series with a Timedelta element.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="s1">'1 days'</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> |
| <span class="go">0 1 days</span> |
| <span class="go">dtype: timedelta64[ns]</span> |
| |
| <span class="go">Select rows with data closest to certain value using argsort (from</span> |
| <span class="go">`StackOverflow <https://stackoverflow.com/a/17758115>`__).</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span> |
| <span class="gp">... </span> <span class="s1">'a'</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'b'</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'c'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="o">-</span><span class="mi">50</span><span class="p">]</span> |
| <span class="gp">... </span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> a b c</span> |
| <span class="go">0 4 10 100</span> |
| <span class="go">1 5 20 50</span> |
| <span class="go">2 6 30 -30</span> |
| <span class="go">3 7 40 -50</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">c</span> <span class="o">-</span> <span class="mi">43</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">argsort</span><span class="p">()]</span> |
| <span class="go"> a b c</span> |
| <span class="go">1 5 20 50</span> |
| <span class="go">0 4 10 100</span> |
| <span class="go">2 6 30 -30</span> |
| <span class="go">3 7 40 -50</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.add"> |
| <code class="descname">add</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.add" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.argsort"> |
| <code class="descname">argsort</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.argsort" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.asfreq"> |
| <code class="descname">asfreq</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.asfreq" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.asof"> |
| <code class="descname">asof</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.asof" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.astype"> |
| <code class="descname">astype</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.astype" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast a pandas object to a specified dtype <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>dtype</strong> (<em>data type</em><em>, or </em><em>dict of column name -> data type</em>) – Use a numpy.dtype or Python type to cast entire pandas object to |
| the same type. Alternatively, use {col: dtype, …}, where col is a |
| column label and dtype is a numpy.dtype or Python type to cast one |
| or more of the DeferredDataFrame’s columns to column-specific types.</li> |
| <li><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Return a copy when <code class="docutils literal notranslate"><span class="pre">copy=True</span></code> (be very careful setting |
| <code class="docutils literal notranslate"><span class="pre">copy=False</span></code> as changes to values then may propagate to other |
| pandas objects).</li> |
| <li><strong>errors</strong> (<em>{'raise'</em><em>, </em><em>'ignore'}</em><em>, </em><em>default 'raise'</em>) – <p>Control raising of exceptions on invalid data for provided dtype.</p> |
| <ul> |
| <li><code class="docutils literal notranslate"><span class="pre">raise</span></code> : allow exceptions to be raised</li> |
| <li><code class="docutils literal notranslate"><span class="pre">ignore</span></code> : suppress exceptions. On error return original object.</li> |
| </ul> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>casted</strong></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">same type as caller</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_datetime()</span></code></dt> |
| <dd>Convert argument to datetime.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_timedelta()</span></code></dt> |
| <dd>Convert argument to timedelta.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_numeric()</span></code></dt> |
| <dd>Convert argument to a numeric type.</dd> |
| <dt><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html#numpy.ndarray.astype" title="(in NumPy v1.20)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">numpy.ndarray.astype()</span></code></a></dt> |
| <dd>Cast a numpy array to a specified type.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Create a DataFrame:</span> |
| |
| <span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'col1'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s1">'col2'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]}</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">d</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">col1 int64</span> |
| <span class="go">col2 int64</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">Cast all columns to int32:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">col1 int32</span> |
| <span class="go">col2 int32</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">Cast col1 to int32 using a dictionary:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">astype</span><span class="p">({</span><span class="s1">'col1'</span><span class="p">:</span> <span class="s1">'int32'</span><span class="p">})</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">col1 int32</span> |
| <span class="go">col2 int64</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">Create a series:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: int32</span> |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int64'</span><span class="p">)</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Convert to categorical type:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">)</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: category</span> |
| <span class="go">Categories (2, int64): [1, 2]</span> |
| |
| <span class="go">Convert to ordered categorical type with custom ordering:</span> |
| |
| <span class="gp">>>> </span><span class="n">cat_dtype</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">types</span><span class="o">.</span><span class="n">CategoricalDtype</span><span class="p">(</span> |
| <span class="gp">... </span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">cat_dtype</span><span class="p">)</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: category</span> |
| <span class="go">Categories (2, int64): [2 < 1]</span> |
| |
| <span class="go">Note that using ``copy=False`` and changing data on a new</span> |
| <span class="go">pandas object may propagate changes:</span> |
| |
| <span class="gp">>>> </span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s2</span> <span class="o">=</span> <span class="n">s1</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int64'</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">s2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10</span> |
| <span class="gp">>>> </span><span class="n">s1</span> <span class="c1"># note that s1[0] has changed too</span> |
| <span class="go">0 10</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Create a series of dates:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser_date</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'20200101'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">ser_date</span> |
| <span class="go">0 2020-01-01</span> |
| <span class="go">1 2020-01-02</span> |
| <span class="go">2 2020-01-03</span> |
| <span class="go">dtype: datetime64[ns]</span> |
| |
| <span class="go">Datetimes are localized to UTC first before</span> |
| <span class="go">converting to the specified timezone:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser_date</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'datetime64[ns, US/Eastern]'</span><span class="p">)</span> |
| <span class="go">0 2019-12-31 19:00:00-05:00</span> |
| <span class="go">1 2020-01-01 19:00:00-05:00</span> |
| <span class="go">2 2020-01-02 19:00:00-05:00</span> |
| <span class="go">dtype: datetime64[ns, US/Eastern]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.at"> |
| <code class="descname">at</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.at" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.at_time"> |
| <code class="descname">at_time</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.at_time" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.attrs"> |
| <code class="descname">attrs</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.attrs" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.attrs is not supported in |
| the Beam DataFrame API because it is experimental in pandas.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.autocorr"> |
| <code class="descname">autocorr</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.autocorr" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.backfill"> |
| <code class="descname">backfill</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.backfill" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.between_time"> |
| <code class="descname">between_time</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.between_time" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.bfill"> |
| <code class="descname">bfill</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.bfill" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.bool"> |
| <code class="descname">bool</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.bool" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.cat"> |
| <code class="descname">cat</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.cat" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.combine"> |
| <code class="descname">combine</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.combine" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.combine_first"> |
| <code class="descname">combine_first</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.combine_first" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.compare"> |
| <code class="descname">compare</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.compare" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.convert_dtypes"> |
| <code class="descname">convert_dtypes</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.convert_dtypes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.copy"> |
| <code class="descname">copy</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.copy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Make a copy of this object’s indices and data.</p> |
| <p>When <code class="docutils literal notranslate"><span class="pre">deep=True</span></code> (default), a new object will be created with a |
| copy of the calling object’s data and indices. Modifications to |
| the data or indices of the copy will not be reflected in the |
| original object (see notes below).</p> |
| <p>When <code class="docutils literal notranslate"><span class="pre">deep=False</span></code>, a new object will be created without copying |
| the calling object’s data or index (only references to the data |
| and index are copied). Any changes to the data of the original |
| will be reflected in the shallow copy (and vice versa).</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>deep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Make a deep copy, including a copy of the data and the indices. |
| With <code class="docutils literal notranslate"><span class="pre">deep=False</span></code> neither the indices nor the data are copied.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>copy</strong> – Object type matches caller.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Notes</p> |
| <p>When <code class="docutils literal notranslate"><span class="pre">deep=True</span></code>, data is copied but actual Python objects |
| will not be copied recursively, only the reference to the object. |
| This is in contrast to <cite>copy.deepcopy</cite> in the Standard Library, |
| which recursively copies object data (see examples below).</p> |
| <p>While <code class="docutils literal notranslate"><span class="pre">Index</span></code> objects are copied when <code class="docutils literal notranslate"><span class="pre">deep=True</span></code>, the underlying |
| numpy array is not copied for performance reasons. Since <code class="docutils literal notranslate"><span class="pre">Index</span></code> is |
| immutable, the underlying data can be safely shared and a copy |
| is not needed.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">a 1</span> |
| <span class="go">b 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">s_copy</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">s_copy</span> |
| <span class="go">a 1</span> |
| <span class="go">b 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">**Shallow copy versus default (deep) copy:**</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">deep</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">shallow</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="go">Shallow copy shares data and index with original.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="ow">is</span> <span class="n">shallow</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">values</span> <span class="ow">is</span> <span class="n">shallow</span><span class="o">.</span><span class="n">values</span> <span class="ow">and</span> <span class="n">s</span><span class="o">.</span><span class="n">index</span> <span class="ow">is</span> <span class="n">shallow</span><span class="o">.</span><span class="n">index</span> |
| <span class="go">True</span> |
| |
| <span class="go">Deep copy has own copy of data and index.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="ow">is</span> <span class="n">deep</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">values</span> <span class="ow">is</span> <span class="n">deep</span><span class="o">.</span><span class="n">values</span> <span class="ow">or</span> <span class="n">s</span><span class="o">.</span><span class="n">index</span> <span class="ow">is</span> <span class="n">deep</span><span class="o">.</span><span class="n">index</span> |
| <span class="go">False</span> |
| |
| <span class="go">Updates to the data shared by shallow copy and original is reflected</span> |
| <span class="go">in both; deep copy remains unchanged.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span> |
| <span class="gp">>>> </span><span class="n">shallow</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">a 3</span> |
| <span class="go">b 4</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="n">shallow</span> |
| <span class="go">a 3</span> |
| <span class="go">b 4</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="n">deep</span> |
| <span class="go">a 1</span> |
| <span class="go">b 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Note that when copying an object containing Python objects, a deep copy</span> |
| <span class="go">will copy the data, but will not do so recursively. Updating a nested</span> |
| <span class="go">data object will be reflected in the deep copy.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">deep</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 [10, 2]</span> |
| <span class="go">1 [3, 4]</span> |
| <span class="go">dtype: object</span> |
| <span class="gp">>>> </span><span class="n">deep</span> |
| <span class="go">0 [10, 2]</span> |
| <span class="go">1 [3, 4]</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.describe"> |
| <code class="descname">describe</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.describe" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.div"> |
| <code class="descname">div</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.div" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.divide"> |
| <code class="descname">divide</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.divide" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.divmod"> |
| <code class="descname">divmod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.divmod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.drop"> |
| <code class="descname">drop</code><span class="sig-paren">(</span><em>labels</em>, <em>axis</em>, <em>index</em>, <em>columns</em>, <em>errors</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.drop" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.drop_duplicates"> |
| <code class="descname">drop_duplicates</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.drop_duplicates" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.droplevel"> |
| <code class="descname">droplevel</code><span class="sig-paren">(</span><em>level</em>, <em>axis</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.droplevel" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.dt"> |
| <code class="descname">dt</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.dt" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.duplicated"> |
| <code class="descname">duplicated</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.duplicated" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.empty"> |
| <code class="descname">empty</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.empty" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.eq"> |
| <code class="descname">eq</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.eq" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return Equal to of series and other, element-wise (binary operator <cite>eq</cite>).</p> |
| <p>Equivalent to <code class="docutils literal notranslate"><span class="pre">series</span> <span class="pre">==</span> <span class="pre">other</span></code>, but with support to substitute a fill_value for |
| missing data in either one of the inputs.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em> or </em><em>scalar value</em>) – </li> |
| <li><strong>fill_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><em>None</em></a><em> or </em><em>float value</em><em>, </em><em>default None</em><em> (</em><em>NaN</em><em>)</em>) – Fill existing missing (NaN) values, and any new element needed for |
| successful DeferredSeries alignment, with this value before computation. |
| If data in both corresponding DeferredSeries locations is missing |
| the result of filling (at that location) will be missing.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>name</em>) – Broadcast across a level, matching Index values on the |
| passed MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result of the operation.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">a</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 1.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">b</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b NaN</span> |
| <span class="go">d 1.0</span> |
| <span class="go">e NaN</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">a True</span> |
| <span class="go">b False</span> |
| <span class="go">c False</span> |
| <span class="go">d False</span> |
| <span class="go">e False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.equals"> |
| <code class="descname">equals</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.equals" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.ewm"> |
| <code class="descname">ewm</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.ewm" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.expanding"> |
| <code class="descname">expanding</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.expanding" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.explode"> |
| <code class="descname">explode</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.explode" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.factorize"> |
| <code class="descname">factorize</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.factorize" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.ffill"> |
| <code class="descname">ffill</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.ffill" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.fillna"> |
| <code class="descname">fillna</code><span class="sig-paren">(</span><em>value</em>, <em>method</em>, <em>axis</em>, <em>limit</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.fillna" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.first_valid_index"> |
| <code class="descname">first_valid_index</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.first_valid_index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.flags"> |
| <code class="descname">flags</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.flags" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.floordiv"> |
| <code class="descname">floordiv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.floordiv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.ge"> |
| <code class="descname">ge</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.ge" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return Greater than or equal to of series and other, element-wise (binary operator <cite>ge</cite>).</p> |
| <p>Equivalent to <code class="docutils literal notranslate"><span class="pre">series</span> <span class="pre">>=</span> <span class="pre">other</span></code>, but with support to substitute a fill_value for |
| missing data in either one of the inputs.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em> or </em><em>scalar value</em>) – </li> |
| <li><strong>fill_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><em>None</em></a><em> or </em><em>float value</em><em>, </em><em>default None</em><em> (</em><em>NaN</em><em>)</em>) – Fill existing missing (NaN) values, and any new element needed for |
| successful DeferredSeries alignment, with this value before computation. |
| If data in both corresponding DeferredSeries locations is missing |
| the result of filling (at that location) will be missing.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>name</em>) – Broadcast across a level, matching Index values on the |
| passed MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result of the operation.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">a</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 1.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">e 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">b</span> |
| <span class="go">a 0.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 2.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">f 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">ge</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">a True</span> |
| <span class="go">b True</span> |
| <span class="go">c False</span> |
| <span class="go">d False</span> |
| <span class="go">e True</span> |
| <span class="go">f False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.get"> |
| <code class="descname">get</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.get" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.groupby"> |
| <code class="descname">groupby</code><span class="sig-paren">(</span><em>by</em>, <em>level</em>, <em>axis</em>, <em>as_index</em>, <em>group_keys</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.groupby" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.gt"> |
| <code class="descname">gt</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.gt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return Greater than of series and other, element-wise (binary operator <cite>gt</cite>).</p> |
| <p>Equivalent to <code class="docutils literal notranslate"><span class="pre">series</span> <span class="pre">></span> <span class="pre">other</span></code>, but with support to substitute a fill_value for |
| missing data in either one of the inputs.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em> or </em><em>scalar value</em>) – </li> |
| <li><strong>fill_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><em>None</em></a><em> or </em><em>float value</em><em>, </em><em>default None</em><em> (</em><em>NaN</em><em>)</em>) – Fill existing missing (NaN) values, and any new element needed for |
| successful DeferredSeries alignment, with this value before computation. |
| If data in both corresponding DeferredSeries locations is missing |
| the result of filling (at that location) will be missing.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>name</em>) – Broadcast across a level, matching Index values on the |
| passed MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result of the operation.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">a</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 1.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">e 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">b</span> |
| <span class="go">a 0.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 2.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">f 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">a True</span> |
| <span class="go">b False</span> |
| <span class="go">c False</span> |
| <span class="go">d False</span> |
| <span class="go">e True</span> |
| <span class="go">f False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.hasnans"> |
| <code class="descname">hasnans</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.hasnans" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.hist"> |
| <code class="descname">hist</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.hist" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.hist is not supported in |
| the Beam DataFrame API because it is a plotting tool.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.iat"> |
| <code class="descname">iat</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.iat" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.idxmax"> |
| <code class="descname">idxmax</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.idxmax" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.idxmin"> |
| <code class="descname">idxmin</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.idxmin" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.iloc"> |
| <code class="descname">iloc</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.iloc" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.index"> |
| <code class="descname">index</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.infer_objects"> |
| <code class="descname">infer_objects</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.infer_objects" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.is_monotonic"> |
| <code class="descname">is_monotonic</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.is_monotonic" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.is_monotonic_decreasing"> |
| <code class="descname">is_monotonic_decreasing</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.is_monotonic_decreasing" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.is_monotonic_increasing"> |
| <code class="descname">is_monotonic_increasing</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.is_monotonic_increasing" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.isin"> |
| <code class="descname">isin</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.isin" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Whether each element in the DataFrame is contained in values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>values</strong> (<em>iterable</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.9)"><em>dict</em></a>) – The result will only be true at a location if all the |
| labels match. If <cite>values</cite> is a DeferredSeries, that’s the index. If |
| <cite>values</cite> is a dict, the keys must be the column names, |
| which must match. If <cite>values</cite> is a DeferredDataFrame, |
| then both the index and column labels must match.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">DeferredDataFrame of booleans showing whether each element in the DeferredDataFrame |
| is contained in values.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Equality test for DeferredDataFrame.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isin" title="apache_beam.dataframe.frames.DeferredSeries.isin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.isin()</span></code></a></dt> |
| <dd>Equivalent method on DeferredSeries.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.str.contains()</span></code></dt> |
| <dd>Test if pattern or regex is contained within a string of a DeferredSeries or Index.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'num_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s1">'num_wings'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'falcon'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon 2 2</span> |
| <span class="go">dog 4 0</span> |
| |
| <span class="go">When ``values`` is a list check whether every value in the DataFrame</span> |
| <span class="go">is present in the list (which animals have 0 or 2 legs or wings)</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon True True</span> |
| <span class="go">dog False True</span> |
| |
| <span class="go">When ``values`` is a dict, we can pass values to check for each</span> |
| <span class="go">column separately:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s1">'num_wings'</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]})</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon False False</span> |
| <span class="go">dog False True</span> |
| |
| <span class="go">When ``values`` is a Series or DataFrame the index and column must</span> |
| <span class="go">match. Note that 'falcon' does not match based on the number of legs</span> |
| <span class="go">in df2.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'num_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s1">'num_wings'</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'spider'</span><span class="p">,</span> <span class="s1">'falcon'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon True True</span> |
| <span class="go">dog False False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.item"> |
| <code class="descname">item</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.item" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.kurt"> |
| <code class="descname">kurt</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.kurt" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.kurtosis"> |
| <code class="descname">kurtosis</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.kurtosis" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.last_valid_index"> |
| <code class="descname">last_valid_index</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.last_valid_index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.le"> |
| <code class="descname">le</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.le" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return Less than or equal to of series and other, element-wise (binary operator <cite>le</cite>).</p> |
| <p>Equivalent to <code class="docutils literal notranslate"><span class="pre">series</span> <span class="pre"><=</span> <span class="pre">other</span></code>, but with support to substitute a fill_value for |
| missing data in either one of the inputs.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em> or </em><em>scalar value</em>) – </li> |
| <li><strong>fill_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><em>None</em></a><em> or </em><em>float value</em><em>, </em><em>default None</em><em> (</em><em>NaN</em><em>)</em>) – Fill existing missing (NaN) values, and any new element needed for |
| successful DeferredSeries alignment, with this value before computation. |
| If data in both corresponding DeferredSeries locations is missing |
| the result of filling (at that location) will be missing.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>name</em>) – Broadcast across a level, matching Index values on the |
| passed MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result of the operation.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">a</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 1.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">e 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">b</span> |
| <span class="go">a 0.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 2.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">f 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">a False</span> |
| <span class="go">b True</span> |
| <span class="go">c True</span> |
| <span class="go">d False</span> |
| <span class="go">e False</span> |
| <span class="go">f True</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.loc"> |
| <code class="descname">loc</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.loc" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.lt"> |
| <code class="descname">lt</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.lt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return Less than of series and other, element-wise (binary operator <cite>lt</cite>).</p> |
| <p>Equivalent to <code class="docutils literal notranslate"><span class="pre">series</span> <span class="pre"><</span> <span class="pre">other</span></code>, but with support to substitute a fill_value for |
| missing data in either one of the inputs.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em> or </em><em>scalar value</em>) – </li> |
| <li><strong>fill_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><em>None</em></a><em> or </em><em>float value</em><em>, </em><em>default None</em><em> (</em><em>NaN</em><em>)</em>) – Fill existing missing (NaN) values, and any new element needed for |
| successful DeferredSeries alignment, with this value before computation. |
| If data in both corresponding DeferredSeries locations is missing |
| the result of filling (at that location) will be missing.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>name</em>) – Broadcast across a level, matching Index values on the |
| passed MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result of the operation.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">a</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 1.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">e 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">b</span> |
| <span class="go">a 0.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 2.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">f 1.0</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">lt</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">a False</span> |
| <span class="go">b False</span> |
| <span class="go">c True</span> |
| <span class="go">d False</span> |
| <span class="go">e False</span> |
| <span class="go">f True</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.mad"> |
| <code class="descname">mad</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.mad" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.mask"> |
| <code class="descname">mask</code><span class="sig-paren">(</span><em>cond</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.mask" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.mod"> |
| <code class="descname">mod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.mod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.mode"> |
| <code class="descname">mode</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.mode" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.mul"> |
| <code class="descname">mul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.mul" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.multiply"> |
| <code class="descname">multiply</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.multiply" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.nbytes"> |
| <code class="descname">nbytes</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.nbytes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.ndim"> |
| <code class="descname">ndim</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.ndim" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.ne"> |
| <code class="descname">ne</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.ne" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return Not equal to of series and other, element-wise (binary operator <cite>ne</cite>).</p> |
| <p>Equivalent to <code class="docutils literal notranslate"><span class="pre">series</span> <span class="pre">!=</span> <span class="pre">other</span></code>, but with support to substitute a fill_value for |
| missing data in either one of the inputs.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em> or </em><em>scalar value</em>) – </li> |
| <li><strong>fill_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><em>None</em></a><em> or </em><em>float value</em><em>, </em><em>default None</em><em> (</em><em>NaN</em><em>)</em>) – Fill existing missing (NaN) values, and any new element needed for |
| successful DeferredSeries alignment, with this value before computation. |
| If data in both corresponding DeferredSeries locations is missing |
| the result of filling (at that location) will be missing.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>name</em>) – Broadcast across a level, matching Index values on the |
| passed MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The result of the operation.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">a</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b 1.0</span> |
| <span class="go">c 1.0</span> |
| <span class="go">d NaN</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">b</span> |
| <span class="go">a 1.0</span> |
| <span class="go">b NaN</span> |
| <span class="go">d 1.0</span> |
| <span class="go">e NaN</span> |
| <span class="go">dtype: float64</span> |
| <span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">a False</span> |
| <span class="go">b True</span> |
| <span class="go">c True</span> |
| <span class="go">d True</span> |
| <span class="go">e True</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.nunique"> |
| <code class="descname">nunique</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.nunique" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.pad"> |
| <code class="descname">pad</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.pad" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.pct_change"> |
| <code class="descname">pct_change</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.pct_change" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.pipe"> |
| <code class="descname">pipe</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.pipe" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.pow"> |
| <code class="descname">pow</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.pow" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.quantile"> |
| <code class="descname">quantile</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.quantile" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.radd"> |
| <code class="descname">radd</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.radd" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rank"> |
| <code class="descname">rank</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rank" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rdiv"> |
| <code class="descname">rdiv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rdiv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rdivmod"> |
| <code class="descname">rdivmod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rdivmod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.reindex"> |
| <code class="descname">reindex</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.reindex" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.reindex_like"> |
| <code class="descname">reindex_like</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.reindex_like" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.reorder_levels"> |
| <code class="descname">reorder_levels</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.reorder_levels" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Rearrange index levels using input order. May not drop or duplicate levels.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>order</strong> (<em>list of int</em><em> or </em><em>list of str</em>) – List representing new level order. Reference level by number |
| (position) or by key (label).</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 0</em>) – Where to reorder levels.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.repeat"> |
| <code class="descname">repeat</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.repeat" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.resample"> |
| <code class="descname">resample</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.resample" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.reset_index"> |
| <code class="descname">reset_index</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.reset_index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rfloordiv"> |
| <code class="descname">rfloordiv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rfloordiv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rmod"> |
| <code class="descname">rmod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rmod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rmul"> |
| <code class="descname">rmul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rmul" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rolling"> |
| <code class="descname">rolling</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rolling" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rpow"> |
| <code class="descname">rpow</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rpow" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rsub"> |
| <code class="descname">rsub</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rsub" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.rtruediv"> |
| <code class="descname">rtruediv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.rtruediv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.sample"> |
| <code class="descname">sample</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.sample" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.sem"> |
| <code class="descname">sem</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.sem" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.set_axis"> |
| <code class="descname">set_axis</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.set_axis" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.set_flags"> |
| <code class="descname">set_flags</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.set_flags" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.shape"> |
| <code class="descname">shape</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.shape" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.size"> |
| <code class="descname">size</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.size" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.skew"> |
| <code class="descname">skew</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.skew" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.slice_shift"> |
| <code class="descname">slice_shift</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.slice_shift" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.sort_index"> |
| <code class="descname">sort_index</code><span class="sig-paren">(</span><em>axis</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.sort_index" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sort object by labels (along an axis).</p> |
| <p>Returns a new DataFrame sorted by label if <cite>inplace</cite> argument is |
| <code class="docutils literal notranslate"><span class="pre">False</span></code>, otherwise updates the original DataFrame and returns None.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 0</em>) – The axis along which to sort. The value 0 identifies the rows, |
| and 1 identifies the columns.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>level name</em><em> or </em><em>list of ints</em><em> or </em><em>list of level names</em>) – If not None, sort on values in specified index level(s).</li> |
| <li><strong>ascending</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em> or </em><em>list-like of bools</em><em>, </em><em>default True</em>) – Sort ascending vs. descending. When the index is a MultiIndex the |
| sort direction can be controlled for each level individually.</li> |
| <li><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default False</em>) – If True, perform operation in-place.</li> |
| <li><strong>kind</strong> (<em>{'quicksort'</em><em>, </em><em>'mergesort'</em><em>, </em><em>'heapsort'}</em><em>, </em><em>default 'quicksort'</em>) – Choice of sorting algorithm. See also ndarray.np.sort for more |
| information. <cite>mergesort</cite> is the only stable algorithm. For |
| DeferredDataFrames, this option is only applied when sorting on a single |
| column or label.</li> |
| <li><strong>na_position</strong> (<em>{'first'</em><em>, </em><em>'last'}</em><em>, </em><em>default 'last'</em>) – Puts NaNs at the beginning if <cite>first</cite>; <cite>last</cite> puts NaNs at the end. |
| Not implemented for MultiIndex.</li> |
| <li><strong>sort_remaining</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – If True and sorting by level and index is multilevel, sort by other |
| levels too (in order) after sorting by specified level.</li> |
| <li><strong>ignore_index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default False</em>) – <p>If True, the resulting axis will be labeled 0, 1, …, n - 1.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified">New in version 1.0.0.</span></p> |
| </div> |
| </li> |
| <li><strong>key</strong> (<em>callable</em><em>, </em><em>optional</em>) – <p>If not None, apply the key function to the index values |
| before sorting. This is similar to the <cite>key</cite> argument in the |
| builtin <code class="xref py py-meth docutils literal notranslate"><span class="pre">sorted()</span></code> function, with the notable difference that |
| this <cite>key</cite> function should be <em>vectorized</em>. It should expect an |
| <code class="docutils literal notranslate"><span class="pre">Index</span></code> and return an <code class="docutils literal notranslate"><span class="pre">Index</span></code> of the same shape. For MultiIndex |
| inputs, the key is applied <em>per level</em>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified">New in version 1.1.0.</span></p> |
| </div> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The original DeferredDataFrame sorted by the labels or None if <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code>.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a> or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)">None</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p><code class="docutils literal notranslate"><span class="pre">axis=index</span></code> is not allowed because it imposes an ordering on the |
| dataset, and we cannot guarantee it will be maintained (see |
| <a class="reference external" href="https://s.apache.org/dataframe-order-sensitive-operations">https://s.apache.org/dataframe-order-sensitive-operations</a>). Only |
| <code class="docutils literal notranslate"><span class="pre">axis=columns</span></code> is allowed.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.sort_index" title="apache_beam.dataframe.frames.DeferredSeries.sort_index"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.sort_index()</span></code></a></dt> |
| <dd>Sort DeferredSeries by the index.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.sort_values" title="apache_beam.dataframe.frames.DeferredDataFrame.sort_values"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.sort_values()</span></code></a></dt> |
| <dd>Sort DeferredDataFrame by the value.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.sort_values" title="apache_beam.dataframe.frames.DeferredSeries.sort_values"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.sort_values()</span></code></a></dt> |
| <dd>Sort DeferredSeries by the value.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API. In addition, some arguments shown here may not be supported, see <strong>‘Differences from pandas’</strong> for details.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">29</span><span class="p">,</span> <span class="mi">234</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">150</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span> |
| <span class="go"> A</span> |
| <span class="go">1 4</span> |
| <span class="go">29 2</span> |
| <span class="go">100 1</span> |
| <span class="go">150 5</span> |
| <span class="go">234 3</span> |
| |
| <span class="go">By default, it sorts in ascending order, to sort in descending order,</span> |
| <span class="go">use ``ascending=False``</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="go"> A</span> |
| <span class="go">234 3</span> |
| <span class="go">150 5</span> |
| <span class="go">100 1</span> |
| <span class="go">29 2</span> |
| <span class="go">1 4</span> |
| |
| <span class="go">A key function can be specified which is applied to the index before</span> |
| <span class="go">sorting. For a ``MultiIndex`` this is applied to each level separately.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">())</span> |
| <span class="go"> a</span> |
| <span class="go">A 1</span> |
| <span class="go">b 2</span> |
| <span class="go">C 3</span> |
| <span class="go">d 4</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.sort_values"> |
| <code class="descname">sort_values</code><span class="sig-paren">(</span><em>axis</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.sort_values" title="Permalink to this definition">¶</a></dt> |
| <dd><p><code class="docutils literal notranslate"><span class="pre">sort_values</span></code> is not implemented.</p> |
| <p>It is not implemented for <code class="docutils literal notranslate"><span class="pre">axis=index</span></code> because it imposes an ordering on |
| the dataset, and we cannot guarantee it will be maintained (see |
| <a class="reference external" href="https://s.apache.org/dataframe-order-sensitive-operations">https://s.apache.org/dataframe-order-sensitive-operations</a>).</p> |
| <p>It is not implemented for <code class="docutils literal notranslate"><span class="pre">axis=columns</span></code> because it makes the order of |
| the columns depend on the data (see |
| <a class="reference external" href="https://s.apache.org/dataframe-non-deferred-column-names">https://s.apache.org/dataframe-non-deferred-column-names</a>).</p> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.sparse"> |
| <code class="descname">sparse</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.sparse" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.squeeze"> |
| <code class="descname">squeeze</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.squeeze" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.sub"> |
| <code class="descname">sub</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.sub" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.subtract"> |
| <code class="descname">subtract</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.subtract" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.swapaxes"> |
| <code class="descname">swapaxes</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.swapaxes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.swaplevel"> |
| <code class="descname">swaplevel</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.swaplevel" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_clipboard"> |
| <code class="descname">to_clipboard</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_clipboard" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_csv"> |
| <code class="descname">to_csv</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_csv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_excel"> |
| <code class="descname">to_excel</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_excel" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_feather"> |
| <code class="descname">to_feather</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_feather" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_hdf"> |
| <code class="descname">to_hdf</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_hdf" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_hdf is not supported in |
| the Beam DataFrame API because HDF5 is a random access file format.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_html"> |
| <code class="descname">to_html</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_html" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_json"> |
| <code class="descname">to_json</code><span class="sig-paren">(</span><em>path</em>, <em>orient=None</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_json" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_latex"> |
| <code class="descname">to_latex</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_latex" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_list"> |
| <code class="descname">to_list</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_list" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_markdown"> |
| <code class="descname">to_markdown</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_markdown" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_msgpack"> |
| <code class="descname">to_msgpack</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_msgpack" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_msgpack is not supported in |
| the Beam DataFrame API because it is deprecated in pandas.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_parquet"> |
| <code class="descname">to_parquet</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_parquet" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_period"> |
| <code class="descname">to_period</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_period" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_pickle"> |
| <code class="descname">to_pickle</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_pickle" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_sql"> |
| <code class="descname">to_sql</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_sql" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_stata"> |
| <code class="descname">to_stata</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_stata" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_timestamp"> |
| <code class="descname">to_timestamp</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_timestamp" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.to_xarray"> |
| <code class="descname">to_xarray</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.to_xarray" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.transform"> |
| <code class="descname">transform</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.transform" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.transpose"> |
| <code class="descname">transpose</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.transpose" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.truediv"> |
| <code class="descname">truediv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.truediv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.truncate"> |
| <code class="descname">truncate</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.truncate" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.tshift"> |
| <code class="descname">tshift</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.tshift" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.tz_convert"> |
| <code class="descname">tz_convert</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.tz_convert" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.tz_localize"> |
| <code class="descname">tz_localize</code><span class="sig-paren">(</span><em>ambiguous</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.tz_localize" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.value_counts"> |
| <code class="descname">value_counts</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.value_counts" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.where"> |
| <code class="descname">where</code><span class="sig-paren">(</span><em>cond</em>, <em>other</em>, <em>errors</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.where" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="classmethod"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.wrap"> |
| <em class="property">classmethod </em><code class="descname">wrap</code><span class="sig-paren">(</span><em>expr</em>, <em>split_tuples=True</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.wrap" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredSeries.xs"> |
| <code class="descname">xs</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredSeries.xs" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame"> |
| <em class="property">class </em><code class="descclassname">apache_beam.dataframe.frames.</code><code class="descname">DeferredDataFrame</code><span class="sig-paren">(</span><em>expr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.dataframe.frames.DeferredDataFrameOrSeries</span></code></p> |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.T"> |
| <code class="descname">T</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.T" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.columns"> |
| <code class="descname">columns</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.columns" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.keys"> |
| <code class="descname">keys</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.keys"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.keys" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.align"> |
| <code class="descname">align</code><span class="sig-paren">(</span><em>other</em>, <em>join</em>, <em>axis</em>, <em>copy</em>, <em>level</em>, <em>method</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.align"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.align" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.append"> |
| <code class="descname">append</code><span class="sig-paren">(</span><em>other</em>, <em>ignore_index</em>, <em>verify_integrity</em>, <em>sort</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.append"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.append" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.set_index"> |
| <code class="descname">set_index</code><span class="sig-paren">(</span><em>keys</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.set_index"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.set_index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.loc"> |
| <code class="descname">loc</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.loc" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.iloc"> |
| <code class="descname">iloc</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.iloc" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.axes"> |
| <code class="descname">axes</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.axes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.dtypes"> |
| <code class="descname">dtypes</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.dtypes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.assign"> |
| <code class="descname">assign</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.assign"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.assign" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.explode"> |
| <code class="descname">explode</code><span class="sig-paren">(</span><em>column</em>, <em>ignore_index</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.explode"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.explode" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.aggregate"> |
| <code class="descname">aggregate</code><span class="sig-paren">(</span><em>func</em>, <em>axis=0</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.aggregate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.aggregate" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.agg"> |
| <code class="descname">agg</code><span class="sig-paren">(</span><em>func</em>, <em>axis=0</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.agg" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.applymap"> |
| <code class="descname">applymap</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.applymap" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Apply a function to a Dataframe elementwise.</p> |
| <p>This method applies a function that accepts and returns a scalar |
| to every element of a DataFrame.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>func</strong> (<em>callable</em>) – Python function, returns a single value from a single value.</li> |
| <li><strong>na_action</strong> (<em>{None</em><em>, </em><em>'ignore'}</em><em>, </em><em>default None</em>) – <p>If ‘ignore’, propagate NaN values, without passing them to func.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified">New in version 1.2.</span></p> |
| </div> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Transformed DeferredDataFrame.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.apply" title="apache_beam.dataframe.frames.DeferredDataFrame.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.apply()</span></code></a></dt> |
| <dd>Apply a function along input axis of DeferredDataFrame.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.12</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.356</span><span class="p">,</span> <span class="mf">4.567</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> 0 1</span> |
| <span class="go">0 1.000 2.120</span> |
| <span class="go">1 3.356 4.567</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> |
| <span class="go"> 0 1</span> |
| <span class="go">0 3 4</span> |
| <span class="go">1 5 5</span> |
| |
| <span class="go">Like Series.map, NA values can be ignored:</span> |
| |
| <span class="gp">>>> </span><span class="n">df_copy</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">df_copy</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">NA</span> |
| <span class="gp">>>> </span><span class="n">df_copy</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="n">na_action</span><span class="o">=</span><span class="s1">'ignore'</span><span class="p">)</span> |
| <span class="go"> 0 1</span> |
| <span class="go">0 <NA> 4</span> |
| <span class="go">1 5 5</span> |
| |
| <span class="go">Note that a vectorized version of `func` often exists, which will</span> |
| <span class="go">be much faster. You could square each number elementwise.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go"> 0 1</span> |
| <span class="go">0 1.000000 4.494400</span> |
| <span class="go">1 11.262736 20.857489</span> |
| |
| <span class="go">But it's better to avoid applymap in that case.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">**</span> <span class="mi">2</span> |
| <span class="go"> 0 1</span> |
| <span class="go">0 1.000000 4.494400</span> |
| <span class="go">1 11.262736 20.857489</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.add_prefix"> |
| <code class="descname">add_prefix</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.add_prefix" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Prefix labels with string <cite>prefix</cite>.</p> |
| <p>For Series, the row labels are prefixed. |
| For DataFrame, the column labels are prefixed.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>prefix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – The string to add before each label.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">New DeferredSeries or DeferredDataFrame with updated labels.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.add_suffix" title="apache_beam.dataframe.frames.DeferredSeries.add_suffix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.add_suffix()</span></code></a></dt> |
| <dd>Suffix row labels with string <cite>suffix</cite>.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.add_suffix" title="apache_beam.dataframe.frames.DeferredDataFrame.add_suffix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.add_suffix()</span></code></a></dt> |
| <dd>Suffix column labels with string <cite>suffix</cite>.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 3</span> |
| <span class="go">3 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s1">'item_'</span><span class="p">)</span> |
| <span class="go">item_0 1</span> |
| <span class="go">item_1 2</span> |
| <span class="go">item_2 3</span> |
| <span class="go">item_3 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s1">'col_'</span><span class="p">)</span> |
| <span class="go"> col_A col_B</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.add_suffix"> |
| <code class="descname">add_suffix</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.add_suffix" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Suffix labels with string <cite>suffix</cite>.</p> |
| <p>For Series, the row labels are suffixed. |
| For DataFrame, the column labels are suffixed.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>suffix</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – The string to add after each label.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">New DeferredSeries or DeferredDataFrame with updated labels.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.add_prefix" title="apache_beam.dataframe.frames.DeferredSeries.add_prefix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.add_prefix()</span></code></a></dt> |
| <dd>Prefix row labels with string <cite>prefix</cite>.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.add_prefix" title="apache_beam.dataframe.frames.DeferredDataFrame.add_prefix"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.add_prefix()</span></code></a></dt> |
| <dd>Prefix column labels with string <cite>prefix</cite>.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 3</span> |
| <span class="go">3 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">add_suffix</span><span class="p">(</span><span class="s1">'_item'</span><span class="p">)</span> |
| <span class="go">0_item 1</span> |
| <span class="go">1_item 2</span> |
| <span class="go">2_item 3</span> |
| <span class="go">3_item 4</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">add_suffix</span><span class="p">(</span><span class="s1">'_col'</span><span class="p">)</span> |
| <span class="go"> A_col B_col</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| <span class="go">2 3 5</span> |
| <span class="go">3 4 6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.memory_usage"> |
| <code class="descname">memory_usage</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.memory_usage" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.memory_usage is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.info"> |
| <code class="descname">info</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.info" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.info is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.clip"> |
| <code class="descname">clip</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.clip" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.corr"> |
| <code class="descname">corr</code><span class="sig-paren">(</span><em>method</em>, <em>min_periods</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.corr"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.corr" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute pairwise correlation of columns, excluding NA/null values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>method</strong> (<em>{'pearson'</em><em>, </em><em>'kendall'</em><em>, </em><em>'spearman'}</em><em> or </em><em>callable</em>) – <p>Method of correlation:</p> |
| <ul> |
| <li>pearson : standard correlation coefficient</li> |
| <li>kendall : Kendall Tau correlation coefficient</li> |
| <li>spearman : Spearman rank correlation</li> |
| <li><dl class="first docutils"> |
| <dt>callable: callable with input two 1d ndarrays</dt> |
| <dd>and returning a float. Note that the returned matrix from corr |
| will have 1 along the diagonals and will be symmetric |
| regardless of the callable’s behavior.<div class="last versionadded"> |
| <p><span class="versionmodified">New in version 0.24.0.</span></p> |
| </div> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </li> |
| <li><strong>min_periods</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – Minimum number of observations required per pair of columns |
| to have a valid result. Currently only available for Pearson |
| and Spearman correlation.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Correlation matrix.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>Only <code class="docutils literal notranslate"><span class="pre">method="pearson"</span></code> can be parallelized. Other methods require |
| collecting all data on a single worker (see |
| <a class="reference external" href="https://s.apache.org/dataframe-non-parallelizable-operations">https://s.apache.org/dataframe-non-parallelizable-operations</a> for details).</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.corrwith" title="apache_beam.dataframe.frames.DeferredDataFrame.corrwith"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.corrwith()</span></code></a></dt> |
| <dd>Compute pairwise correlation with another DeferredDataFrame or DeferredSeries.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.corr" title="apache_beam.dataframe.frames.DeferredSeries.corr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.corr()</span></code></a></dt> |
| <dd>Compute the correlation between two DeferredSeries.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API. In addition, some arguments shown here may not be supported, see <strong>‘Differences from pandas’</strong> for details.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">histogram_intersection</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span> |
| <span class="gp">... </span> <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">decimals</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="gp">... </span> <span class="k">return</span> <span class="n">v</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([(</span><span class="mf">.2</span><span class="p">,</span> <span class="mf">.3</span><span class="p">),</span> <span class="p">(</span><span class="mf">.0</span><span class="p">,</span> <span class="mf">.6</span><span class="p">),</span> <span class="p">(</span><span class="mf">.6</span><span class="p">,</span> <span class="mf">.0</span><span class="p">),</span> <span class="p">(</span><span class="mf">.2</span><span class="p">,</span> <span class="mf">.1</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'dogs'</span><span class="p">,</span> <span class="s1">'cats'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">histogram_intersection</span><span class="p">)</span> |
| <span class="go"> dogs cats</span> |
| <span class="go">dogs 1.0 0.3</span> |
| <span class="go">cats 0.3 1.0</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.cov"> |
| <code class="descname">cov</code><span class="sig-paren">(</span><em>min_periods</em>, <em>ddof</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.cov"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.cov" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.corrwith"> |
| <code class="descname">corrwith</code><span class="sig-paren">(</span><em>other</em>, <em>axis</em>, <em>drop</em>, <em>method</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.corrwith"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.corrwith" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.cummax"> |
| <code class="descname">cummax</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.cummax" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.cummax is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.cummin"> |
| <code class="descname">cummin</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.cummin" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.cummin is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.cumprod"> |
| <code class="descname">cumprod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.cumprod" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.cumprod is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.cumsum"> |
| <code class="descname">cumsum</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.cumsum" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.cumsum is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.diff"> |
| <code class="descname">diff</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.diff" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.diff is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.first"> |
| <code class="descname">first</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.first" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.first is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.head"> |
| <code class="descname">head</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.head" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.head is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.interpolate"> |
| <code class="descname">interpolate</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.interpolate" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.interpolate is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.last"> |
| <code class="descname">last</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.last" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.last is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.tail"> |
| <code class="descname">tail</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.tail" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.tail is not supported in |
| the Beam DataFrame API because it is sensitive to the order of the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.dot"> |
| <code class="descname">dot</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.dot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.dot" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.mode"> |
| <code class="descname">mode</code><span class="sig-paren">(</span><em>axis=0</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.mode"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.mode" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.dropna"> |
| <code class="descname">dropna</code><span class="sig-paren">(</span><em>axis</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.dropna"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.dropna" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.eval"> |
| <code class="descname">eval</code><span class="sig-paren">(</span><em>expr</em>, <em>inplace</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.eval"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.eval" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.query"> |
| <code class="descname">query</code><span class="sig-paren">(</span><em>expr</em>, <em>inplace</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.query"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.query" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.isnull"> |
| <code class="descname">isnull</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.isnull" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect missing values.</p> |
| <p>Return a boolean same-sized object indicating if the values are NA. |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, gets mapped to True |
| values. |
| Everything else gets mapped to False values. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>).</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredDataFrame that |
| indicates whether an element is an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.isnull" title="apache_beam.dataframe.frames.DeferredDataFrame.isnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.isnull()</span></code></a></dt> |
| <dd>Alias of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.notna" title="apache_beam.dataframe.frames.DeferredDataFrame.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.notna()</span></code></a></dt> |
| <dd>Boolean inverse of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.dropna" title="apache_beam.dataframe.frames.DeferredDataFrame.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.isna" title="apache_beam.dataframe.frames.DeferredDataFrame.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">isna()</span></code></a></dt> |
| <dd>Top-level isna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 False True False True</span> |
| <span class="go">1 False False False False</span> |
| <span class="go">2 True False False False</span> |
| |
| <span class="go">Show which entries in a Series are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go">0 False</span> |
| <span class="go">1 False</span> |
| <span class="go">2 True</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.isna"> |
| <code class="descname">isna</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.isna" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect missing values.</p> |
| <p>Return a boolean same-sized object indicating if the values are NA. |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, gets mapped to True |
| values. |
| Everything else gets mapped to False values. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>).</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredDataFrame that |
| indicates whether an element is an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.isnull" title="apache_beam.dataframe.frames.DeferredDataFrame.isnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.isnull()</span></code></a></dt> |
| <dd>Alias of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.notna" title="apache_beam.dataframe.frames.DeferredDataFrame.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.notna()</span></code></a></dt> |
| <dd>Boolean inverse of isna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.dropna" title="apache_beam.dataframe.frames.DeferredDataFrame.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.isna" title="apache_beam.dataframe.frames.DeferredDataFrame.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">isna()</span></code></a></dt> |
| <dd>Top-level isna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 False True False True</span> |
| <span class="go">1 False False False False</span> |
| <span class="go">2 True False False False</span> |
| |
| <span class="go">Show which entries in a Series are NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span> |
| <span class="go">0 False</span> |
| <span class="go">1 False</span> |
| <span class="go">2 True</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.notnull"> |
| <code class="descname">notnull</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.notnull" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect existing (non-missing) values.</p> |
| <p>Return a boolean same-sized object indicating if the values are not NA. |
| Non-missing values get mapped to True. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>). |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, get mapped to False |
| values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredDataFrame that |
| indicates whether an element is not an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.notnull" title="apache_beam.dataframe.frames.DeferredDataFrame.notnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.notnull()</span></code></a></dt> |
| <dd>Alias of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.isna" title="apache_beam.dataframe.frames.DeferredDataFrame.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.isna()</span></code></a></dt> |
| <dd>Boolean inverse of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.dropna" title="apache_beam.dataframe.frames.DeferredDataFrame.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.notna" title="apache_beam.dataframe.frames.DeferredDataFrame.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">notna()</span></code></a></dt> |
| <dd>Top-level notna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 True False True False</span> |
| <span class="go">1 True True True True</span> |
| <span class="go">2 False True True True</span> |
| |
| <span class="go">Show which entries in a Series are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go">0 True</span> |
| <span class="go">1 True</span> |
| <span class="go">2 False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.notna"> |
| <code class="descname">notna</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.notna" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Detect existing (non-missing) values.</p> |
| <p>Return a boolean same-sized object indicating if the values are not NA. |
| Non-missing values get mapped to True. Characters such as empty |
| strings <code class="docutils literal notranslate"><span class="pre">''</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.inf</span></code> are not considered NA values |
| (unless you set <code class="docutils literal notranslate"><span class="pre">pandas.options.mode.use_inf_as_na</span> <span class="pre">=</span> <span class="pre">True</span></code>). |
| NA values, such as None or <code class="xref py py-attr docutils literal notranslate"><span class="pre">numpy.NaN</span></code>, get mapped to False |
| values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Mask of bool values for each element in DeferredDataFrame that |
| indicates whether an element is not an NA value.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.notnull" title="apache_beam.dataframe.frames.DeferredDataFrame.notnull"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.notnull()</span></code></a></dt> |
| <dd>Alias of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.isna" title="apache_beam.dataframe.frames.DeferredDataFrame.isna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.isna()</span></code></a></dt> |
| <dd>Boolean inverse of notna.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.dropna" title="apache_beam.dataframe.frames.DeferredDataFrame.dropna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.dropna()</span></code></a></dt> |
| <dd>Omit axes labels with missing values.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.notna" title="apache_beam.dataframe.frames.DeferredDataFrame.notna"><code class="xref py py-meth docutils literal notranslate"><span class="pre">notna()</span></code></a></dt> |
| <dd>Top-level notna.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Show which entries in a DataFrame are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">age</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">born</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">NaT</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1939-05-27'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'1940-04-25'</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">name</span><span class="o">=</span><span class="p">[</span><span class="s1">'Alfred'</span><span class="p">,</span> <span class="s1">'Batman'</span><span class="p">,</span> <span class="s1">''</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">toy</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">'Batmobile'</span><span class="p">,</span> <span class="s1">'Joker'</span><span class="p">]))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 5.0 NaT Alfred None</span> |
| <span class="go">1 6.0 1939-05-27 Batman Batmobile</span> |
| <span class="go">2 NaN 1940-04-25 Joker</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go"> age born name toy</span> |
| <span class="go">0 True False True False</span> |
| <span class="go">1 True True True True</span> |
| <span class="go">2 False True True True</span> |
| |
| <span class="go">Show which entries in a Series are not NA.</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 5.0</span> |
| <span class="go">1 6.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">notna</span><span class="p">()</span> |
| <span class="go">0 True</span> |
| <span class="go">1 True</span> |
| <span class="go">2 False</span> |
| <span class="go">dtype: bool</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.items"> |
| <code class="descname">items</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.items" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.items is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.itertuples"> |
| <code class="descname">itertuples</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.itertuples" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.itertuples is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.iterrows"> |
| <code class="descname">iterrows</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.iterrows" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.iterrows is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.iteritems"> |
| <code class="descname">iteritems</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.iteritems" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.iteritems is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.join"> |
| <code class="descname">join</code><span class="sig-paren">(</span><em>other</em>, <em>on</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.join"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.join" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.merge"> |
| <code class="descname">merge</code><span class="sig-paren">(</span><em>right</em>, <em>on</em>, <em>left_on</em>, <em>right_on</em>, <em>left_index</em>, <em>right_index</em>, <em>suffixes</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.merge"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.merge" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.nlargest"> |
| <code class="descname">nlargest</code><span class="sig-paren">(</span><em>keep</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.nlargest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.nlargest" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.nsmallest"> |
| <code class="descname">nsmallest</code><span class="sig-paren">(</span><em>keep</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.nsmallest"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.nsmallest" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.nunique"> |
| <code class="descname">nunique</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.nunique"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.nunique" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.plot"> |
| <code class="descname">plot</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.plot" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.plot is not supported in |
| the Beam DataFrame API because it is a plotting tool.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.pop"> |
| <code class="descname">pop</code><span class="sig-paren">(</span><em>item</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.pop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.pop" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.quantile"> |
| <code class="descname">quantile</code><span class="sig-paren">(</span><em>q</em>, <em>axis</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.quantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.quantile" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rename"> |
| <code class="descname">rename</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.rename"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rename" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rename_axis"> |
| <code class="descname">rename_axis</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rename_axis" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set the name of the axis for the index or columns.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>mapper</strong> (<em>scalar</em><em>, </em><em>list-like</em><em>, </em><em>optional</em>) – Value to set the axis name attribute.</li> |
| <li><strong>columns</strong> (<em>index</em><em>,</em>) – <p>A scalar, list-like, dict-like or functions transformations to |
| apply to that axis’ values. |
| Note that the <code class="docutils literal notranslate"><span class="pre">columns</span></code> parameter is not allowed if the |
| object is a DeferredSeries. This parameter only apply for DeferredDataFrame |
| type objects.</p> |
| <p>Use either <code class="docutils literal notranslate"><span class="pre">mapper</span></code> and <code class="docutils literal notranslate"><span class="pre">axis</span></code> to |
| specify the axis to target with <code class="docutils literal notranslate"><span class="pre">mapper</span></code>, or <code class="docutils literal notranslate"><span class="pre">index</span></code> |
| and/or <code class="docutils literal notranslate"><span class="pre">columns</span></code>.</p> |
| <div class="versionchanged"> |
| <p><span class="versionmodified">Changed in version 0.24.0.</span></p> |
| </div> |
| </li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 0</em>) – The axis to rename.</li> |
| <li><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Also copy underlying data.</li> |
| <li><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default False</em>) – Modifies the object directly, instead of creating a new DeferredSeries |
| or DeferredDataFrame.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The same type as the caller or None if <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code>.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a>, <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a>, or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)">None</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.rename" title="apache_beam.dataframe.frames.DeferredSeries.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.rename()</span></code></a></dt> |
| <dd>Alter DeferredSeries index labels or name.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.rename" title="apache_beam.dataframe.frames.DeferredDataFrame.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.rename()</span></code></a></dt> |
| <dd>Alter DeferredDataFrame index labels or name.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">Index.rename()</span></code></dt> |
| <dd>Set new names on index.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p><code class="docutils literal notranslate"><span class="pre">DeferredDataFrame.rename_axis</span></code> supports two calling conventions</p> |
| <ul class="simple"> |
| <li><code class="docutils literal notranslate"><span class="pre">(index=index_mapper,</span> <span class="pre">columns=columns_mapper,</span> <span class="pre">...)</span></code></li> |
| <li><code class="docutils literal notranslate"><span class="pre">(mapper,</span> <span class="pre">axis={'index',</span> <span class="pre">'columns'},</span> <span class="pre">...)</span></code></li> |
| </ul> |
| <p>The first calling convention will only modify the names of |
| the index and/or the names of the Index object that is the columns. |
| In this case, the parameter <code class="docutils literal notranslate"><span class="pre">copy</span></code> is ignored.</p> |
| <p>The second calling convention will modify the names of the |
| corresponding index if mapper is a list or a scalar. |
| However, if mapper is dict-like or a function, it will use the |
| deprecated behavior of modifying the axis <em>labels</em>.</p> |
| <p>We <em>highly</em> recommend using keyword arguments to clarify your |
| intent.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">**Series**</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s2">"dog"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"monkey"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 dog</span> |
| <span class="go">1 cat</span> |
| <span class="go">2 monkey</span> |
| <span class="go">dtype: object</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="s2">"animal"</span><span class="p">)</span> |
| <span class="go">animal</span> |
| <span class="go">0 dog</span> |
| <span class="go">1 cat</span> |
| <span class="go">2 monkey</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">**DataFrame**</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"num_legs"</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s2">"num_arms"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"dog"</span><span class="p">,</span> <span class="s2">"cat"</span><span class="p">,</span> <span class="s2">"monkey"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> num_legs num_arms</span> |
| <span class="go">dog 4 0</span> |
| <span class="go">cat 4 0</span> |
| <span class="go">monkey 2 2</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="s2">"animal"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> num_legs num_arms</span> |
| <span class="go">animal</span> |
| <span class="go">dog 4 0</span> |
| <span class="go">cat 4 0</span> |
| <span class="go">monkey 2 2</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="s2">"limbs"</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s2">"columns"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go">limbs num_legs num_arms</span> |
| <span class="go">animal</span> |
| <span class="go">dog 4 0</span> |
| <span class="go">cat 4 0</span> |
| <span class="go">monkey 2 2</span> |
| |
| <span class="go">**MultiIndex**</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_product</span><span class="p">([[</span><span class="s1">'mammal'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'dog'</span><span class="p">,</span> <span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'monkey'</span><span class="p">]],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'type'</span><span class="p">,</span> <span class="s1">'name'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go">limbs num_legs num_arms</span> |
| <span class="go">type name</span> |
| <span class="go">mammal dog 4 0</span> |
| <span class="go"> cat 4 0</span> |
| <span class="go"> monkey 2 2</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="s1">'class'</span><span class="p">})</span> |
| <span class="go">limbs num_legs num_arms</span> |
| <span class="go">class name</span> |
| <span class="go">mammal dog 4 0</span> |
| <span class="go"> cat 4 0</span> |
| <span class="go"> monkey 2 2</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="nb">str</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span> |
| <span class="go">LIMBS num_legs num_arms</span> |
| <span class="go">type name</span> |
| <span class="go">mammal dog 4 0</span> |
| <span class="go"> cat 4 0</span> |
| <span class="go"> monkey 2 2</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.replace"> |
| <code class="descname">replace</code><span class="sig-paren">(</span><em>limit</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.replace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.replace" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.reset_index"> |
| <code class="descname">reset_index</code><span class="sig-paren">(</span><em>level=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.reset_index"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.reset_index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.round"> |
| <code class="descname">round</code><span class="sig-paren">(</span><em>decimals</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.round"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.round" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.select_dtypes"> |
| <code class="descname">select_dtypes</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.select_dtypes" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a subset of the DataFrame’s columns based on the column dtypes.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>exclude</strong> (<em>include</em><em>,</em>) – A selection of dtypes or strings to be included/excluded. At least |
| one of these parameters must be supplied.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The subset of the frame including the dtypes in <code class="docutils literal notranslate"><span class="pre">include</span></code> and |
| excluding the dtypes in <code class="docutils literal notranslate"><span class="pre">exclude</span></code>.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Raises:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.9)"><code class="xref py py-exc docutils literal notranslate"><span class="pre">ValueError</span></code></a> – * If both of <code class="docutils literal notranslate"><span class="pre">include</span></code> and <code class="docutils literal notranslate"><span class="pre">exclude</span></code> are empty |
| * If <code class="docutils literal notranslate"><span class="pre">include</span></code> and <code class="docutils literal notranslate"><span class="pre">exclude</span></code> have overlapping elements |
| * If any kind of string dtype is passed in.</td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.dtypes" title="apache_beam.dataframe.frames.DeferredDataFrame.dtypes"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.dtypes()</span></code></a></dt> |
| <dd>Return DeferredSeries with the data type of each column.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <ul class="simple"> |
| <li>To select all <em>numeric</em> types, use <code class="docutils literal notranslate"><span class="pre">np.number</span></code> or <code class="docutils literal notranslate"><span class="pre">'number'</span></code></li> |
| <li>To select strings you must use the <code class="docutils literal notranslate"><span class="pre">object</span></code> dtype, but note that |
| this will return <em>all</em> object dtype columns</li> |
| <li>See the <a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html">numpy dtype hierarchy</a></li> |
| <li>To select datetimes, use <code class="docutils literal notranslate"><span class="pre">np.datetime64</span></code>, <code class="docutils literal notranslate"><span class="pre">'datetime'</span></code> or |
| <code class="docutils literal notranslate"><span class="pre">'datetime64'</span></code></li> |
| <li>To select timedeltas, use <code class="docutils literal notranslate"><span class="pre">np.timedelta64</span></code>, <code class="docutils literal notranslate"><span class="pre">'timedelta'</span></code> or |
| <code class="docutils literal notranslate"><span class="pre">'timedelta64'</span></code></li> |
| <li>To select Pandas categorical dtypes, use <code class="docutils literal notranslate"><span class="pre">'category'</span></code></li> |
| <li>To select Pandas datetimetz dtypes, use <code class="docutils literal notranslate"><span class="pre">'datetimetz'</span></code> (new in |
| 0.20.0) or <code class="docutils literal notranslate"><span class="pre">'datetime64[ns,</span> <span class="pre">tz]'</span></code></li> |
| </ul> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'a'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="s1">'b'</span><span class="p">:</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="s1">'c'</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> a b c</span> |
| <span class="go">0 1 True 1.0</span> |
| <span class="go">1 2 False 2.0</span> |
| <span class="go">2 1 True 1.0</span> |
| <span class="go">3 2 False 2.0</span> |
| <span class="go">4 1 True 1.0</span> |
| <span class="go">5 2 False 2.0</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s1">'bool'</span><span class="p">)</span> |
| <span class="go"> b</span> |
| <span class="go">0 True</span> |
| <span class="go">1 False</span> |
| <span class="go">2 True</span> |
| <span class="go">3 False</span> |
| <span class="go">4 True</span> |
| <span class="go">5 False</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s1">'float64'</span><span class="p">])</span> |
| <span class="go"> c</span> |
| <span class="go">0 1.0</span> |
| <span class="go">1 2.0</span> |
| <span class="go">2 1.0</span> |
| <span class="go">3 2.0</span> |
| <span class="go">4 1.0</span> |
| <span class="go">5 2.0</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">exclude</span><span class="o">=</span><span class="p">[</span><span class="s1">'int64'</span><span class="p">])</span> |
| <span class="go"> b c</span> |
| <span class="go">0 True 1.0</span> |
| <span class="go">1 False 2.0</span> |
| <span class="go">2 True 1.0</span> |
| <span class="go">3 False 2.0</span> |
| <span class="go">4 True 1.0</span> |
| <span class="go">5 False 2.0</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.shift"> |
| <code class="descname">shift</code><span class="sig-paren">(</span><em>axis</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.shift"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.shift" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.shape"> |
| <code class="descname">shape</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.shape is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.stack"> |
| <code class="descname">stack</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.stack" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Stack the prescribed level(s) from columns to index.</p> |
| <p>Return a reshaped DataFrame or Series having a multi-level |
| index with one or more new inner-most levels compared to the current |
| DataFrame. The new inner-most levels are created by pivoting the |
| columns of the current dataframe:</p> |
| <blockquote> |
| <div><ul class="simple"> |
| <li>if the columns have a single level, the output is a Series;</li> |
| <li>if the columns have multiple levels, the new index |
| level(s) is (are) taken from the prescribed level(s) and |
| the output is a DataFrame.</li> |
| </ul> |
| </div></blockquote> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.9)"><em>list</em></a><em>, </em><em>default -1</em>) – Level(s) to stack from the column axis onto the index |
| axis, defined as one index or label, or a list of indices |
| or labels.</li> |
| <li><strong>dropna</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Whether to drop rows in the resulting Frame/DeferredSeries with |
| missing values. Stacking a column level onto the index |
| axis can create combinations of index and column values |
| that are missing from the original dataframe. See Examples |
| section.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Stacked dataframe or series.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.unstack" title="apache_beam.dataframe.frames.DeferredDataFrame.unstack"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.unstack()</span></code></a></dt> |
| <dd>Unstack prescribed level(s) from index axis onto column axis.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.pivot" title="apache_beam.dataframe.frames.DeferredDataFrame.pivot"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.pivot()</span></code></a></dt> |
| <dd>Reshape dataframe from long format to wide format.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.pivot_table" title="apache_beam.dataframe.frames.DeferredDataFrame.pivot_table"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.pivot_table()</span></code></a></dt> |
| <dd>Create a spreadsheet-style pivot table as a DeferredDataFrame.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>The function is named by analogy with a collection of books |
| being reorganized from being side by side on a horizontal |
| position (the columns of the dataframe) to being stacked |
| vertically on top of each other (in the index of the |
| dataframe).</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">**Single level columns**</span> |
| |
| <span class="gp">>>> </span><span class="n">df_single_level_cols</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'weight'</span><span class="p">,</span> <span class="s1">'height'</span><span class="p">])</span> |
| |
| <span class="go">Stacking a dataframe with a single level column axis returns a Series:</span> |
| |
| <span class="gp">>>> </span><span class="n">df_single_level_cols</span> |
| <span class="go"> weight height</span> |
| <span class="go">cat 0 1</span> |
| <span class="go">dog 2 3</span> |
| <span class="gp">>>> </span><span class="n">df_single_level_cols</span><span class="o">.</span><span class="n">stack</span><span class="p">()</span> |
| <span class="go">cat weight 0</span> |
| <span class="go"> height 1</span> |
| <span class="go">dog weight 2</span> |
| <span class="go"> height 3</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">**Multi level columns: simple case**</span> |
| |
| <span class="gp">>>> </span><span class="n">multicol1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">([(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="s1">'kg'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="s1">'pounds'</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="n">multicol1</span><span class="p">)</span> |
| |
| <span class="go">Stacking a dataframe with a multi-level column axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols1</span> |
| <span class="go"> weight</span> |
| <span class="go"> kg pounds</span> |
| <span class="go">cat 1 2</span> |
| <span class="go">dog 2 4</span> |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols1</span><span class="o">.</span><span class="n">stack</span><span class="p">()</span> |
| <span class="go"> weight</span> |
| <span class="go">cat kg 1</span> |
| <span class="go"> pounds 2</span> |
| <span class="go">dog kg 2</span> |
| <span class="go"> pounds 4</span> |
| |
| <span class="go">**Missing values**</span> |
| |
| <span class="gp">>>> </span><span class="n">multicol2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">([(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="s1">'kg'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s1">'height'</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]],</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="n">multicol2</span><span class="p">)</span> |
| |
| <span class="go">It is common to have missing values when stacking a dataframe</span> |
| <span class="go">with multi-level columns, as the stacked dataframe typically</span> |
| <span class="go">has more values than the original dataframe. Missing values</span> |
| <span class="go">are filled with NaNs:</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols2</span> |
| <span class="go"> weight height</span> |
| <span class="go"> kg m</span> |
| <span class="go">cat 1.0 2.0</span> |
| <span class="go">dog 3.0 4.0</span> |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols2</span><span class="o">.</span><span class="n">stack</span><span class="p">()</span> |
| <span class="go"> height weight</span> |
| <span class="go">cat kg NaN 1.0</span> |
| <span class="go"> m 2.0 NaN</span> |
| <span class="go">dog kg NaN 3.0</span> |
| <span class="go"> m 4.0 NaN</span> |
| |
| <span class="go">**Prescribing the level(s) to be stacked**</span> |
| |
| <span class="go">The first parameter controls which level or levels are stacked:</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols2</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go"> kg m</span> |
| <span class="go">cat height NaN 2.0</span> |
| <span class="go"> weight 1.0 NaN</span> |
| <span class="go">dog height NaN 4.0</span> |
| <span class="go"> weight 3.0 NaN</span> |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols2</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> |
| <span class="go">cat height m 2.0</span> |
| <span class="go"> weight kg 1.0</span> |
| <span class="go">dog height m 4.0</span> |
| <span class="go"> weight kg 3.0</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="go">**Dropping missing values**</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="kc">None</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]],</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="n">multicol2</span><span class="p">)</span> |
| |
| <span class="go">Note that rows where all values are missing are dropped by</span> |
| <span class="go">default but this behaviour can be controlled via the dropna</span> |
| <span class="go">keyword parameter:</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols3</span> |
| <span class="go"> weight height</span> |
| <span class="go"> kg m</span> |
| <span class="go">cat NaN 1.0</span> |
| <span class="go">dog 2.0 3.0</span> |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols3</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="go"> height weight</span> |
| <span class="go">cat kg NaN NaN</span> |
| <span class="go"> m 1.0 NaN</span> |
| <span class="go">dog kg NaN 2.0</span> |
| <span class="go"> m 3.0 NaN</span> |
| <span class="gp">>>> </span><span class="n">df_multi_level_cols3</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="go"> height weight</span> |
| <span class="go">cat m 1.0 NaN</span> |
| <span class="go">dog kg NaN 2.0</span> |
| <span class="go"> m 3.0 NaN</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.all"> |
| <code class="descname">all</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.all" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.any"> |
| <code class="descname">any</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.any" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.count"> |
| <code class="descname">count</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.count" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.max"> |
| <code class="descname">max</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.max" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.min"> |
| <code class="descname">min</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.min" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.prod"> |
| <code class="descname">prod</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.prod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.product"> |
| <code class="descname">product</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.product" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.sum"> |
| <code class="descname">sum</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.sum" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.mean"> |
| <code class="descname">mean</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.mean" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.median"> |
| <code class="descname">median</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.median" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.take"> |
| <code class="descname">take</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.take" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.take is not supported in |
| the Beam DataFrame API because it is deprecated in pandas.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_records"> |
| <code class="descname">to_records</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_records" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_records is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_dict"> |
| <code class="descname">to_dict</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_dict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_dict is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_numpy"> |
| <code class="descname">to_numpy</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_numpy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_numpy is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_string"> |
| <code class="descname">to_string</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_string" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_string is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_sparse"> |
| <code class="descname">to_sparse</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_sparse" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_sparse is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.transpose"> |
| <code class="descname">transpose</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.transpose" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.transpose is not supported in |
| the Beam DataFrame API because the columns in the output DataFrame depend on the data.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.unstack"> |
| <code class="descname">unstack</code><span class="sig-paren">(</span><em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/frames.html#DeferredDataFrame.unstack"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.unstack" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.update"> |
| <code class="descname">update</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Modify in place using non-NA values from another DataFrame.</p> |
| <p>Aligns on indices. There is no return value.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a><em>, or </em><em>object coercible into a DeferredDataFrame</em>) – Should have at least one matching index/column label |
| with the original DeferredDataFrame. If a DeferredSeries is passed, |
| its name attribute must be set, and that will be |
| used as the column name to align with the original DeferredDataFrame.</li> |
| <li><strong>join</strong> (<em>{'left'}</em><em>, </em><em>default 'left'</em>) – Only left join is implemented, keeping the index and columns of the |
| original object.</li> |
| <li><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – <p>How to handle non-NA values for overlapping keys:</p> |
| <ul> |
| <li>True: overwrite original DeferredDataFrame’s values |
| with values from <cite>other</cite>.</li> |
| <li>False: only update values that are NA in |
| the original DeferredDataFrame.</li> |
| </ul> |
| </li> |
| <li><strong>filter_func</strong> (<em>callable</em><em>(</em><em>1d-array</em><em>) </em><em>-> bool 1d-array</em><em>, </em><em>optional</em>) – Can choose to replace values other than NA. Return True for values |
| that should be updated.</li> |
| <li><strong>errors</strong> (<em>{'raise'</em><em>, </em><em>'ignore'}</em><em>, </em><em>default 'ignore'</em>) – <p>If ‘raise’, will raise a ValueError if the DeferredDataFrame and <cite>other</cite> |
| both contain non-NA data in the same place.</p> |
| <div class="versionchanged"> |
| <p><span class="versionmodified">Changed in version 0.24.0: </span>Changed from <cite>raise_conflict=False|True</cite> |
| to <cite>errors=’ignore’|’raise’</cite>.</p> |
| </div> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>None</strong></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first">method directly changes calling object</p> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Raises:</th><td class="field-body"><ul class="first last simple"> |
| <li><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.9)"><code class="xref py py-exc docutils literal notranslate"><span class="pre">ValueError</span></code></a> – * When <cite>errors=’raise’</cite> and there’s overlapping non-NA data. |
| * When <cite>errors</cite> is not either <cite>‘ignore’</cite> or <cite>‘raise’</cite></li> |
| <li><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.9)"><code class="xref py py-exc docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a> – * If <cite>join != ‘left’</cite></li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict.update" title="(in Python v3.9)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dict.update()</span></code></a></dt> |
| <dd>Similar method for dictionaries.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.merge" title="apache_beam.dataframe.frames.DeferredDataFrame.merge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.merge()</span></code></a></dt> |
| <dd>For column(s)-on-column(s) operations.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">600</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">new_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'C'</span><span class="p">:</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 1 4</span> |
| <span class="go">1 2 5</span> |
| <span class="go">2 3 6</span> |
| |
| <span class="go">The DataFrame's length does not increase as a result of the update,</span> |
| <span class="go">only values at matching index/column labels are updated.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span> <span class="s1">'z'</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">new_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">,</span> <span class="s1">'g'</span><span class="p">,</span> <span class="s1">'h'</span><span class="p">,</span> <span class="s1">'i'</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 a d</span> |
| <span class="go">1 b e</span> |
| <span class="go">2 c f</span> |
| |
| <span class="go">For Series, its name attribute must be set.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span> <span class="s1">'z'</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">new_column</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">'B'</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_column</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 a d</span> |
| <span class="go">1 b y</span> |
| <span class="go">2 c e</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span> <span class="s1">'z'</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">new_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'d'</span><span class="p">,</span> <span class="s1">'e'</span><span class="p">]},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 a x</span> |
| <span class="go">1 b d</span> |
| <span class="go">2 c e</span> |
| |
| <span class="go">If `other` contains NaNs the corresponding values are not updated</span> |
| <span class="go">in the original dataframe.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'A'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">600</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">new_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'B'</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 1 4.0</span> |
| <span class="go">1 2 500.0</span> |
| <span class="go">2 3 6.0</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.values"> |
| <code class="descname">values</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.values" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.values is not supported in |
| the Beam DataFrame API because it produces an output type that is not deferred.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.abs"> |
| <code class="descname">abs</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.abs" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a Series/DataFrame with absolute numeric value of each element.</p> |
| <p>This function only applies to elements that are all numeric.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">DeferredSeries/DeferredDataFrame containing the absolute value of each element.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">abs</td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">numpy.absolute()</span></code></dt> |
| <dd>Calculate the absolute value element-wise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>For <code class="docutils literal notranslate"><span class="pre">complex</span></code> inputs, <code class="docutils literal notranslate"><span class="pre">1.2</span> <span class="pre">+</span> <span class="pre">1j</span></code>, the absolute value is |
| <span class="math notranslate nohighlight">\(\sqrt{ a^2 + b^2 }\)</span>.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Absolute numeric values in a Series.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="o">-</span><span class="mf">1.10</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.33</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> |
| <span class="go">0 1.10</span> |
| <span class="go">1 2.00</span> |
| <span class="go">2 3.33</span> |
| <span class="go">3 4.00</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="go">Absolute numeric values in a Series with complex numbers.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mf">1.2</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> |
| <span class="go">0 1.56205</span> |
| <span class="go">dtype: float64</span> |
| |
| <span class="go">Absolute numeric values in a Series with a Timedelta element.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="s1">'1 days'</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> |
| <span class="go">0 1 days</span> |
| <span class="go">dtype: timedelta64[ns]</span> |
| |
| <span class="go">Select rows with data closest to certain value using argsort (from</span> |
| <span class="go">`StackOverflow <https://stackoverflow.com/a/17758115>`__).</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span> |
| <span class="gp">... </span> <span class="s1">'a'</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'b'</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'c'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="o">-</span><span class="mi">50</span><span class="p">]</span> |
| <span class="gp">... </span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> a b c</span> |
| <span class="go">0 4 10 100</span> |
| <span class="go">1 5 20 50</span> |
| <span class="go">2 6 30 -30</span> |
| <span class="go">3 7 40 -50</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">c</span> <span class="o">-</span> <span class="mi">43</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">argsort</span><span class="p">()]</span> |
| <span class="go"> a b c</span> |
| <span class="go">1 5 20 50</span> |
| <span class="go">0 4 10 100</span> |
| <span class="go">2 6 30 -30</span> |
| <span class="go">3 7 40 -50</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.add"> |
| <code class="descname">add</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.add" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.apply"> |
| <code class="descname">apply</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.apply" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.asfreq"> |
| <code class="descname">asfreq</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.asfreq" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.asof"> |
| <code class="descname">asof</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.asof" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.astype"> |
| <code class="descname">astype</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.astype" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast a pandas object to a specified dtype <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>dtype</strong> (<em>data type</em><em>, or </em><em>dict of column name -> data type</em>) – Use a numpy.dtype or Python type to cast entire pandas object to |
| the same type. Alternatively, use {col: dtype, …}, where col is a |
| column label and dtype is a numpy.dtype or Python type to cast one |
| or more of the DeferredDataFrame’s columns to column-specific types.</li> |
| <li><strong>copy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Return a copy when <code class="docutils literal notranslate"><span class="pre">copy=True</span></code> (be very careful setting |
| <code class="docutils literal notranslate"><span class="pre">copy=False</span></code> as changes to values then may propagate to other |
| pandas objects).</li> |
| <li><strong>errors</strong> (<em>{'raise'</em><em>, </em><em>'ignore'}</em><em>, </em><em>default 'raise'</em>) – <p>Control raising of exceptions on invalid data for provided dtype.</p> |
| <ul> |
| <li><code class="docutils literal notranslate"><span class="pre">raise</span></code> : allow exceptions to be raised</li> |
| <li><code class="docutils literal notranslate"><span class="pre">ignore</span></code> : suppress exceptions. On error return original object.</li> |
| </ul> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>casted</strong></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">same type as caller</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_datetime()</span></code></dt> |
| <dd>Convert argument to datetime.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_timedelta()</span></code></dt> |
| <dd>Convert argument to timedelta.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_numeric()</span></code></dt> |
| <dd>Convert argument to a numeric type.</dd> |
| <dt><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html#numpy.ndarray.astype" title="(in NumPy v1.20)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">numpy.ndarray.astype()</span></code></a></dt> |
| <dd>Cast a numpy array to a specified type.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="go">Create a DataFrame:</span> |
| |
| <span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'col1'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s1">'col2'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]}</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">d</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">col1 int64</span> |
| <span class="go">col2 int64</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">Cast all columns to int32:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">col1 int32</span> |
| <span class="go">col2 int32</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">Cast col1 to int32 using a dictionary:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">astype</span><span class="p">({</span><span class="s1">'col1'</span><span class="p">:</span> <span class="s1">'int32'</span><span class="p">})</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">col1 int32</span> |
| <span class="go">col2 int64</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="go">Create a series:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">ser</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: int32</span> |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int64'</span><span class="p">)</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Convert to categorical type:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'category'</span><span class="p">)</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: category</span> |
| <span class="go">Categories (2, int64): [1, 2]</span> |
| |
| <span class="go">Convert to ordered categorical type with custom ordering:</span> |
| |
| <span class="gp">>>> </span><span class="n">cat_dtype</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">types</span><span class="o">.</span><span class="n">CategoricalDtype</span><span class="p">(</span> |
| <span class="gp">... </span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">ser</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">cat_dtype</span><span class="p">)</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: category</span> |
| <span class="go">Categories (2, int64): [2 < 1]</span> |
| |
| <span class="go">Note that using ``copy=False`` and changing data on a new</span> |
| <span class="go">pandas object may propagate changes:</span> |
| |
| <span class="gp">>>> </span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s2</span> <span class="o">=</span> <span class="n">s1</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int64'</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">s2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10</span> |
| <span class="gp">>>> </span><span class="n">s1</span> <span class="c1"># note that s1[0] has changed too</span> |
| <span class="go">0 10</span> |
| <span class="go">1 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Create a series of dates:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser_date</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'20200101'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">ser_date</span> |
| <span class="go">0 2020-01-01</span> |
| <span class="go">1 2020-01-02</span> |
| <span class="go">2 2020-01-03</span> |
| <span class="go">dtype: datetime64[ns]</span> |
| |
| <span class="go">Datetimes are localized to UTC first before</span> |
| <span class="go">converting to the specified timezone:</span> |
| |
| <span class="gp">>>> </span><span class="n">ser_date</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'datetime64[ns, US/Eastern]'</span><span class="p">)</span> |
| <span class="go">0 2019-12-31 19:00:00-05:00</span> |
| <span class="go">1 2020-01-01 19:00:00-05:00</span> |
| <span class="go">2 2020-01-02 19:00:00-05:00</span> |
| <span class="go">dtype: datetime64[ns, US/Eastern]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.at"> |
| <code class="descname">at</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.at" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.at_time"> |
| <code class="descname">at_time</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.at_time" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.attrs"> |
| <code class="descname">attrs</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.attrs" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.attrs is not supported in |
| the Beam DataFrame API because it is experimental in pandas.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.backfill"> |
| <code class="descname">backfill</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.backfill" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.between_time"> |
| <code class="descname">between_time</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.between_time" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.bfill"> |
| <code class="descname">bfill</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.bfill" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.bool"> |
| <code class="descname">bool</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.bool" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.boxplot"> |
| <code class="descname">boxplot</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.boxplot" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.combine"> |
| <code class="descname">combine</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.combine" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.combine_first"> |
| <code class="descname">combine_first</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.combine_first" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.compare"> |
| <code class="descname">compare</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.compare" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.convert_dtypes"> |
| <code class="descname">convert_dtypes</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.convert_dtypes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.copy"> |
| <code class="descname">copy</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.copy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Make a copy of this object’s indices and data.</p> |
| <p>When <code class="docutils literal notranslate"><span class="pre">deep=True</span></code> (default), a new object will be created with a |
| copy of the calling object’s data and indices. Modifications to |
| the data or indices of the copy will not be reflected in the |
| original object (see notes below).</p> |
| <p>When <code class="docutils literal notranslate"><span class="pre">deep=False</span></code>, a new object will be created without copying |
| the calling object’s data or index (only references to the data |
| and index are copied). Any changes to the data of the original |
| will be reflected in the shallow copy (and vice versa).</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>deep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – Make a deep copy, including a copy of the data and the indices. |
| With <code class="docutils literal notranslate"><span class="pre">deep=False</span></code> neither the indices nor the data are copied.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>copy</strong> – Object type matches caller.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries">DeferredSeries</a> or <a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <p class="rubric">Notes</p> |
| <p>When <code class="docutils literal notranslate"><span class="pre">deep=True</span></code>, data is copied but actual Python objects |
| will not be copied recursively, only the reference to the object. |
| This is in contrast to <cite>copy.deepcopy</cite> in the Standard Library, |
| which recursively copies object data (see examples below).</p> |
| <p>While <code class="docutils literal notranslate"><span class="pre">Index</span></code> objects are copied when <code class="docutils literal notranslate"><span class="pre">deep=True</span></code>, the underlying |
| numpy array is not copied for performance reasons. Since <code class="docutils literal notranslate"><span class="pre">Index</span></code> is |
| immutable, the underlying data can be safely shared and a copy |
| is not needed.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">a 1</span> |
| <span class="go">b 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="gp">>>> </span><span class="n">s_copy</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">s_copy</span> |
| <span class="go">a 1</span> |
| <span class="go">b 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">**Shallow copy versus default (deep) copy:**</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">deep</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">shallow</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="go">Shallow copy shares data and index with original.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="ow">is</span> <span class="n">shallow</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">values</span> <span class="ow">is</span> <span class="n">shallow</span><span class="o">.</span><span class="n">values</span> <span class="ow">and</span> <span class="n">s</span><span class="o">.</span><span class="n">index</span> <span class="ow">is</span> <span class="n">shallow</span><span class="o">.</span><span class="n">index</span> |
| <span class="go">True</span> |
| |
| <span class="go">Deep copy has own copy of data and index.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="ow">is</span> <span class="n">deep</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="o">.</span><span class="n">values</span> <span class="ow">is</span> <span class="n">deep</span><span class="o">.</span><span class="n">values</span> <span class="ow">or</span> <span class="n">s</span><span class="o">.</span><span class="n">index</span> <span class="ow">is</span> <span class="n">deep</span><span class="o">.</span><span class="n">index</span> |
| <span class="go">False</span> |
| |
| <span class="go">Updates to the data shared by shallow copy and original is reflected</span> |
| <span class="go">in both; deep copy remains unchanged.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span> |
| <span class="gp">>>> </span><span class="n">shallow</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">a 3</span> |
| <span class="go">b 4</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="n">shallow</span> |
| <span class="go">a 3</span> |
| <span class="go">b 4</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="n">deep</span> |
| <span class="go">a 1</span> |
| <span class="go">b 2</span> |
| <span class="go">dtype: int64</span> |
| |
| <span class="go">Note that when copying an object containing Python objects, a deep copy</span> |
| <span class="go">will copy the data, but will not do so recursively. Updating a nested</span> |
| <span class="go">data object will be reflected in the deep copy.</span> |
| |
| <span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">deep</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10</span> |
| <span class="gp">>>> </span><span class="n">s</span> |
| <span class="go">0 [10, 2]</span> |
| <span class="go">1 [3, 4]</span> |
| <span class="go">dtype: object</span> |
| <span class="gp">>>> </span><span class="n">deep</span> |
| <span class="go">0 [10, 2]</span> |
| <span class="go">1 [3, 4]</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.describe"> |
| <code class="descname">describe</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.describe" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.div"> |
| <code class="descname">div</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.div" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.divide"> |
| <code class="descname">divide</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.divide" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.drop"> |
| <code class="descname">drop</code><span class="sig-paren">(</span><em>labels</em>, <em>axis</em>, <em>index</em>, <em>columns</em>, <em>errors</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.drop" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.drop_duplicates"> |
| <code class="descname">drop_duplicates</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.drop_duplicates" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.droplevel"> |
| <code class="descname">droplevel</code><span class="sig-paren">(</span><em>level</em>, <em>axis</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.droplevel" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.dtype"> |
| <code class="descname">dtype</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.dtype" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.duplicated"> |
| <code class="descname">duplicated</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.duplicated" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.empty"> |
| <code class="descname">empty</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.empty" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.eq"> |
| <code class="descname">eq</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get Equal to of dataframe and other, element-wise (binary operator <cite>eq</cite>).</p> |
| <p>Among flexible wrappers (<cite>eq</cite>, <cite>ne</cite>, <cite>le</cite>, <cite>lt</cite>, <cite>ge</cite>, <cite>gt</cite>) to comparison |
| operators.</p> |
| <p>Equivalent to <cite>==</cite>, <cite>!=</cite>, <cite><=</cite>, <cite><</cite>, <cite>>=</cite>, <cite>></cite> with support to choose axis |
| (rows or columns) and level for comparison.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<em>scalar</em><em>, </em><em>sequence</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a>) – Any single or multiple element data structure, or list-like object.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 'columns'</em>) – Whether to compare by the index (0 or ‘index’) or columns |
| (1 or ‘columns’).</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>label</em>) – Broadcast across a level, matching Index values on the passed |
| MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Result of the comparison.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DeferredDataFrame of bool</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ne" title="apache_beam.dataframe.frames.DeferredDataFrame.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ne()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.le" title="apache_beam.dataframe.frames.DeferredDataFrame.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.le()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for less than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.lt" title="apache_beam.dataframe.frames.DeferredDataFrame.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.lt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly less than inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ge" title="apache_beam.dataframe.frames.DeferredDataFrame.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ge()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for greater than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.gt" title="apache_beam.dataframe.frames.DeferredDataFrame.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.gt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly greater than inequality elementwise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>Mismatched indices will be unioned together. |
| <cite>NaN</cite> values are considered different (i.e. <cite>NaN</cite> != <cite>NaN</cite>).</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A 250 100</span> |
| <span class="go">B 150 250</span> |
| <span class="go">C 100 300</span> |
| |
| <span class="go">Comparison with a scalar, using either the operator or method:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="mi">100</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">When `other` is a :class:`Series`, the columns of a DataFrame are aligned</span> |
| <span class="go">with the index of `other` and broadcast:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">!=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"cost"</span><span class="p">,</span> <span class="s2">"revenue"</span><span class="p">])</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B True False</span> |
| <span class="go">C False True</span> |
| |
| <span class="go">Use the method to control the broadcast axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B True True</span> |
| <span class="go">C True True</span> |
| <span class="go">D True True</span> |
| |
| <span class="go">When comparing to an arbitrary sequence, the number of columns must</span> |
| <span class="go">match the number elements in `other`:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B False False</span> |
| <span class="go">C False False</span> |
| |
| <span class="go">Use the method to control the axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">([</span><span class="mi">250</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B False True</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">Compare to a DataFrame of different shape.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">other</span> |
| <span class="go"> revenue</span> |
| <span class="go">A 300</span> |
| <span class="go">B 250</span> |
| <span class="go">C 100</span> |
| <span class="go">D 150</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False False</span> |
| <span class="go">B False False</span> |
| <span class="go">C False True</span> |
| <span class="go">D False False</span> |
| |
| <span class="go">Compare to a MultiIndex by level.</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multindex</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">220</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">175</span><span class="p">,</span> <span class="mi">225</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[[</span><span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">df_multindex</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A 250 100</span> |
| <span class="go"> B 150 250</span> |
| <span class="go"> C 100 300</span> |
| <span class="go">Q2 A 150 200</span> |
| <span class="go"> B 300 175</span> |
| <span class="go"> C 220 225</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">df_multindex</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A True True</span> |
| <span class="go"> B True True</span> |
| <span class="go"> C True True</span> |
| <span class="go">Q2 A False True</span> |
| <span class="go"> B True False</span> |
| <span class="go"> C True False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.equals"> |
| <code class="descname">equals</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.equals" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.ewm"> |
| <code class="descname">ewm</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.ewm" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.expanding"> |
| <code class="descname">expanding</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.expanding" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.ffill"> |
| <code class="descname">ffill</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.ffill" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.fillna"> |
| <code class="descname">fillna</code><span class="sig-paren">(</span><em>value</em>, <em>method</em>, <em>axis</em>, <em>limit</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.fillna" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.filter"> |
| <code class="descname">filter</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.filter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Subset the dataframe rows or columns according to the specified index labels.</p> |
| <p>Note that this routine does not filter a dataframe on its |
| contents. The filter is applied to the labels of the index.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>items</strong> (<em>list-like</em>) – Keep labels from axis which are in items.</li> |
| <li><strong>like</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – Keep labels from axis for which “like in label == True”.</li> |
| <li><strong>regex</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em> (</em><em>regular expression</em><em>)</em>) – Keep labels from axis for which re.search(regex, label) == True.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>‘index’</em><em>, </em><em>1</em><em> or </em><em>‘columns’</em><em>, </em><em>None}</em><em>, </em><em>default None</em>) – The axis to filter on, expressed either as an index (int) |
| or axis name (str). By default this is the info axis, |
| ‘index’ for DeferredSeries, ‘columns’ for DeferredDataFrame.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">same type as input object</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.loc" title="apache_beam.dataframe.frames.DeferredDataFrame.loc"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.loc()</span></code></a></dt> |
| <dd>Access a group of rows and columns by label(s) or a boolean array.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>The <code class="docutils literal notranslate"><span class="pre">items</span></code>, <code class="docutils literal notranslate"><span class="pre">like</span></code>, and <code class="docutils literal notranslate"><span class="pre">regex</span></code> parameters are |
| enforced to be mutually exclusive.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">axis</span></code> defaults to the info axis that is used when indexing |
| with <code class="docutils literal notranslate"><span class="pre">[]</span></code>.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])),</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'mouse'</span><span class="p">,</span> <span class="s1">'rabbit'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'two'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> one two three</span> |
| <span class="go">mouse 1 2 3</span> |
| <span class="go">rabbit 4 5 6</span> |
| |
| <span class="gp">>>> </span><span class="c1"># select columns by name</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">items</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span> |
| <span class="go"> one three</span> |
| <span class="go">mouse 1 3</span> |
| <span class="go">rabbit 4 6</span> |
| |
| <span class="gp">>>> </span><span class="c1"># select columns by regular expression</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">regex</span><span class="o">=</span><span class="s1">'e$'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> one three</span> |
| <span class="go">mouse 1 3</span> |
| <span class="go">rabbit 4 6</span> |
| |
| <span class="gp">>>> </span><span class="c1"># select rows containing 'bbi'</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'bbi'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go"> one two three</span> |
| <span class="go">rabbit 4 5 6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.first_valid_index"> |
| <code class="descname">first_valid_index</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.first_valid_index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.flags"> |
| <code class="descname">flags</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.flags" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.floordiv"> |
| <code class="descname">floordiv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.floordiv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.from_dict"> |
| <code class="descname">from_dict</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.from_dict" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.from_records"> |
| <code class="descname">from_records</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.from_records" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.ge"> |
| <code class="descname">ge</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.ge" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get Greater than or equal to of dataframe and other, element-wise (binary operator <cite>ge</cite>).</p> |
| <p>Among flexible wrappers (<cite>eq</cite>, <cite>ne</cite>, <cite>le</cite>, <cite>lt</cite>, <cite>ge</cite>, <cite>gt</cite>) to comparison |
| operators.</p> |
| <p>Equivalent to <cite>==</cite>, <cite>!=</cite>, <cite><=</cite>, <cite><</cite>, <cite>>=</cite>, <cite>></cite> with support to choose axis |
| (rows or columns) and level for comparison.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<em>scalar</em><em>, </em><em>sequence</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a>) – Any single or multiple element data structure, or list-like object.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 'columns'</em>) – Whether to compare by the index (0 or ‘index’) or columns |
| (1 or ‘columns’).</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>label</em>) – Broadcast across a level, matching Index values on the passed |
| MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Result of the comparison.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DeferredDataFrame of bool</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ne" title="apache_beam.dataframe.frames.DeferredDataFrame.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ne()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.le" title="apache_beam.dataframe.frames.DeferredDataFrame.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.le()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for less than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.lt" title="apache_beam.dataframe.frames.DeferredDataFrame.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.lt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly less than inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ge" title="apache_beam.dataframe.frames.DeferredDataFrame.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ge()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for greater than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.gt" title="apache_beam.dataframe.frames.DeferredDataFrame.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.gt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly greater than inequality elementwise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>Mismatched indices will be unioned together. |
| <cite>NaN</cite> values are considered different (i.e. <cite>NaN</cite> != <cite>NaN</cite>).</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A 250 100</span> |
| <span class="go">B 150 250</span> |
| <span class="go">C 100 300</span> |
| |
| <span class="go">Comparison with a scalar, using either the operator or method:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="mi">100</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">When `other` is a :class:`Series`, the columns of a DataFrame are aligned</span> |
| <span class="go">with the index of `other` and broadcast:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">!=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"cost"</span><span class="p">,</span> <span class="s2">"revenue"</span><span class="p">])</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B True False</span> |
| <span class="go">C False True</span> |
| |
| <span class="go">Use the method to control the broadcast axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B True True</span> |
| <span class="go">C True True</span> |
| <span class="go">D True True</span> |
| |
| <span class="go">When comparing to an arbitrary sequence, the number of columns must</span> |
| <span class="go">match the number elements in `other`:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B False False</span> |
| <span class="go">C False False</span> |
| |
| <span class="go">Use the method to control the axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">([</span><span class="mi">250</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B False True</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">Compare to a DataFrame of different shape.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">other</span> |
| <span class="go"> revenue</span> |
| <span class="go">A 300</span> |
| <span class="go">B 250</span> |
| <span class="go">C 100</span> |
| <span class="go">D 150</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False False</span> |
| <span class="go">B False False</span> |
| <span class="go">C False True</span> |
| <span class="go">D False False</span> |
| |
| <span class="go">Compare to a MultiIndex by level.</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multindex</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">220</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">175</span><span class="p">,</span> <span class="mi">225</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[[</span><span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">df_multindex</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A 250 100</span> |
| <span class="go"> B 150 250</span> |
| <span class="go"> C 100 300</span> |
| <span class="go">Q2 A 150 200</span> |
| <span class="go"> B 300 175</span> |
| <span class="go"> C 220 225</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">df_multindex</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A True True</span> |
| <span class="go"> B True True</span> |
| <span class="go"> C True True</span> |
| <span class="go">Q2 A False True</span> |
| <span class="go"> B True False</span> |
| <span class="go"> C True False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.get"> |
| <code class="descname">get</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.get" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.groupby"> |
| <code class="descname">groupby</code><span class="sig-paren">(</span><em>by</em>, <em>level</em>, <em>axis</em>, <em>as_index</em>, <em>group_keys</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.groupby" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.gt"> |
| <code class="descname">gt</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.gt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get Greater than of dataframe and other, element-wise (binary operator <cite>gt</cite>).</p> |
| <p>Among flexible wrappers (<cite>eq</cite>, <cite>ne</cite>, <cite>le</cite>, <cite>lt</cite>, <cite>ge</cite>, <cite>gt</cite>) to comparison |
| operators.</p> |
| <p>Equivalent to <cite>==</cite>, <cite>!=</cite>, <cite><=</cite>, <cite><</cite>, <cite>>=</cite>, <cite>></cite> with support to choose axis |
| (rows or columns) and level for comparison.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<em>scalar</em><em>, </em><em>sequence</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a>) – Any single or multiple element data structure, or list-like object.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 'columns'</em>) – Whether to compare by the index (0 or ‘index’) or columns |
| (1 or ‘columns’).</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>label</em>) – Broadcast across a level, matching Index values on the passed |
| MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Result of the comparison.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DeferredDataFrame of bool</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ne" title="apache_beam.dataframe.frames.DeferredDataFrame.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ne()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.le" title="apache_beam.dataframe.frames.DeferredDataFrame.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.le()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for less than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.lt" title="apache_beam.dataframe.frames.DeferredDataFrame.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.lt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly less than inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ge" title="apache_beam.dataframe.frames.DeferredDataFrame.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ge()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for greater than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.gt" title="apache_beam.dataframe.frames.DeferredDataFrame.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.gt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly greater than inequality elementwise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>Mismatched indices will be unioned together. |
| <cite>NaN</cite> values are considered different (i.e. <cite>NaN</cite> != <cite>NaN</cite>).</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A 250 100</span> |
| <span class="go">B 150 250</span> |
| <span class="go">C 100 300</span> |
| |
| <span class="go">Comparison with a scalar, using either the operator or method:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="mi">100</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">When `other` is a :class:`Series`, the columns of a DataFrame are aligned</span> |
| <span class="go">with the index of `other` and broadcast:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">!=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"cost"</span><span class="p">,</span> <span class="s2">"revenue"</span><span class="p">])</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B True False</span> |
| <span class="go">C False True</span> |
| |
| <span class="go">Use the method to control the broadcast axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B True True</span> |
| <span class="go">C True True</span> |
| <span class="go">D True True</span> |
| |
| <span class="go">When comparing to an arbitrary sequence, the number of columns must</span> |
| <span class="go">match the number elements in `other`:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B False False</span> |
| <span class="go">C False False</span> |
| |
| <span class="go">Use the method to control the axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">([</span><span class="mi">250</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B False True</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">Compare to a DataFrame of different shape.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">other</span> |
| <span class="go"> revenue</span> |
| <span class="go">A 300</span> |
| <span class="go">B 250</span> |
| <span class="go">C 100</span> |
| <span class="go">D 150</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False False</span> |
| <span class="go">B False False</span> |
| <span class="go">C False True</span> |
| <span class="go">D False False</span> |
| |
| <span class="go">Compare to a MultiIndex by level.</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multindex</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">220</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">175</span><span class="p">,</span> <span class="mi">225</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[[</span><span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">df_multindex</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A 250 100</span> |
| <span class="go"> B 150 250</span> |
| <span class="go"> C 100 300</span> |
| <span class="go">Q2 A 150 200</span> |
| <span class="go"> B 300 175</span> |
| <span class="go"> C 220 225</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">df_multindex</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A True True</span> |
| <span class="go"> B True True</span> |
| <span class="go"> C True True</span> |
| <span class="go">Q2 A False True</span> |
| <span class="go"> B True False</span> |
| <span class="go"> C True False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.hist"> |
| <code class="descname">hist</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.hist" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.hist is not supported in |
| the Beam DataFrame API because it is a plotting tool.</p> |
| <p>For more information see {reason_data[‘url’]}.</p> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.iat"> |
| <code class="descname">iat</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.iat" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.idxmax"> |
| <code class="descname">idxmax</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.idxmax" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.idxmin"> |
| <code class="descname">idxmin</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.idxmin" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.index"> |
| <code class="descname">index</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.infer_objects"> |
| <code class="descname">infer_objects</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.infer_objects" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.insert"> |
| <code class="descname">insert</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.insert" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.isin"> |
| <code class="descname">isin</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.isin" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Whether each element in the DataFrame is contained in values.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>values</strong> (<em>iterable</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.9)"><em>dict</em></a>) – The result will only be true at a location if all the |
| labels match. If <cite>values</cite> is a DeferredSeries, that’s the index. If |
| <cite>values</cite> is a dict, the keys must be the column names, |
| which must match. If <cite>values</cite> is a DeferredDataFrame, |
| then both the index and column labels must match.</td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">DeferredDataFrame of booleans showing whether each element in the DeferredDataFrame |
| is contained in values.</td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Equality test for DeferredDataFrame.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.isin" title="apache_beam.dataframe.frames.DeferredSeries.isin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.isin()</span></code></a></dt> |
| <dd>Equivalent method on DeferredSeries.</dd> |
| <dt><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.str.contains()</span></code></dt> |
| <dd>Test if pattern or regex is contained within a string of a DeferredSeries or Index.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'num_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s1">'num_wings'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'falcon'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon 2 2</span> |
| <span class="go">dog 4 0</span> |
| |
| <span class="go">When ``values`` is a list check whether every value in the DataFrame</span> |
| <span class="go">is present in the list (which animals have 0 or 2 legs or wings)</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon True True</span> |
| <span class="go">dog False True</span> |
| |
| <span class="go">When ``values`` is a dict, we can pass values to check for each</span> |
| <span class="go">column separately:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s1">'num_wings'</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]})</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon False False</span> |
| <span class="go">dog False True</span> |
| |
| <span class="go">When ``values`` is a Series or DataFrame the index and column must</span> |
| <span class="go">match. Note that 'falcon' does not match based on the number of legs</span> |
| <span class="go">in df2.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'num_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s1">'num_wings'</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'spider'</span><span class="p">,</span> <span class="s1">'falcon'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> num_legs num_wings</span> |
| <span class="go">falcon True True</span> |
| <span class="go">dog False False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.kurt"> |
| <code class="descname">kurt</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.kurt" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.kurtosis"> |
| <code class="descname">kurtosis</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.kurtosis" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.last_valid_index"> |
| <code class="descname">last_valid_index</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.last_valid_index" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.le"> |
| <code class="descname">le</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.le" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get Less than or equal to of dataframe and other, element-wise (binary operator <cite>le</cite>).</p> |
| <p>Among flexible wrappers (<cite>eq</cite>, <cite>ne</cite>, <cite>le</cite>, <cite>lt</cite>, <cite>ge</cite>, <cite>gt</cite>) to comparison |
| operators.</p> |
| <p>Equivalent to <cite>==</cite>, <cite>!=</cite>, <cite><=</cite>, <cite><</cite>, <cite>>=</cite>, <cite>></cite> with support to choose axis |
| (rows or columns) and level for comparison.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<em>scalar</em><em>, </em><em>sequence</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a>) – Any single or multiple element data structure, or list-like object.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 'columns'</em>) – Whether to compare by the index (0 or ‘index’) or columns |
| (1 or ‘columns’).</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>label</em>) – Broadcast across a level, matching Index values on the passed |
| MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Result of the comparison.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DeferredDataFrame of bool</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ne" title="apache_beam.dataframe.frames.DeferredDataFrame.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ne()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.le" title="apache_beam.dataframe.frames.DeferredDataFrame.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.le()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for less than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.lt" title="apache_beam.dataframe.frames.DeferredDataFrame.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.lt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly less than inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ge" title="apache_beam.dataframe.frames.DeferredDataFrame.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ge()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for greater than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.gt" title="apache_beam.dataframe.frames.DeferredDataFrame.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.gt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly greater than inequality elementwise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>Mismatched indices will be unioned together. |
| <cite>NaN</cite> values are considered different (i.e. <cite>NaN</cite> != <cite>NaN</cite>).</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A 250 100</span> |
| <span class="go">B 150 250</span> |
| <span class="go">C 100 300</span> |
| |
| <span class="go">Comparison with a scalar, using either the operator or method:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="mi">100</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">When `other` is a :class:`Series`, the columns of a DataFrame are aligned</span> |
| <span class="go">with the index of `other` and broadcast:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">!=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"cost"</span><span class="p">,</span> <span class="s2">"revenue"</span><span class="p">])</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B True False</span> |
| <span class="go">C False True</span> |
| |
| <span class="go">Use the method to control the broadcast axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B True True</span> |
| <span class="go">C True True</span> |
| <span class="go">D True True</span> |
| |
| <span class="go">When comparing to an arbitrary sequence, the number of columns must</span> |
| <span class="go">match the number elements in `other`:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B False False</span> |
| <span class="go">C False False</span> |
| |
| <span class="go">Use the method to control the axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">([</span><span class="mi">250</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B False True</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">Compare to a DataFrame of different shape.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">other</span> |
| <span class="go"> revenue</span> |
| <span class="go">A 300</span> |
| <span class="go">B 250</span> |
| <span class="go">C 100</span> |
| <span class="go">D 150</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False False</span> |
| <span class="go">B False False</span> |
| <span class="go">C False True</span> |
| <span class="go">D False False</span> |
| |
| <span class="go">Compare to a MultiIndex by level.</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multindex</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">220</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">175</span><span class="p">,</span> <span class="mi">225</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[[</span><span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">df_multindex</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A 250 100</span> |
| <span class="go"> B 150 250</span> |
| <span class="go"> C 100 300</span> |
| <span class="go">Q2 A 150 200</span> |
| <span class="go"> B 300 175</span> |
| <span class="go"> C 220 225</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">df_multindex</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A True True</span> |
| <span class="go"> B True True</span> |
| <span class="go"> C True True</span> |
| <span class="go">Q2 A False True</span> |
| <span class="go"> B True False</span> |
| <span class="go"> C True False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.lookup"> |
| <code class="descname">lookup</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.lookup" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.lt"> |
| <code class="descname">lt</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.lt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get Less than of dataframe and other, element-wise (binary operator <cite>lt</cite>).</p> |
| <p>Among flexible wrappers (<cite>eq</cite>, <cite>ne</cite>, <cite>le</cite>, <cite>lt</cite>, <cite>ge</cite>, <cite>gt</cite>) to comparison |
| operators.</p> |
| <p>Equivalent to <cite>==</cite>, <cite>!=</cite>, <cite><=</cite>, <cite><</cite>, <cite>>=</cite>, <cite>></cite> with support to choose axis |
| (rows or columns) and level for comparison.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<em>scalar</em><em>, </em><em>sequence</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a>) – Any single or multiple element data structure, or list-like object.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 'columns'</em>) – Whether to compare by the index (0 or ‘index’) or columns |
| (1 or ‘columns’).</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>label</em>) – Broadcast across a level, matching Index values on the passed |
| MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Result of the comparison.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DeferredDataFrame of bool</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ne" title="apache_beam.dataframe.frames.DeferredDataFrame.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ne()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.le" title="apache_beam.dataframe.frames.DeferredDataFrame.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.le()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for less than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.lt" title="apache_beam.dataframe.frames.DeferredDataFrame.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.lt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly less than inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ge" title="apache_beam.dataframe.frames.DeferredDataFrame.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ge()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for greater than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.gt" title="apache_beam.dataframe.frames.DeferredDataFrame.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.gt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly greater than inequality elementwise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>Mismatched indices will be unioned together. |
| <cite>NaN</cite> values are considered different (i.e. <cite>NaN</cite> != <cite>NaN</cite>).</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A 250 100</span> |
| <span class="go">B 150 250</span> |
| <span class="go">C 100 300</span> |
| |
| <span class="go">Comparison with a scalar, using either the operator or method:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="mi">100</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">When `other` is a :class:`Series`, the columns of a DataFrame are aligned</span> |
| <span class="go">with the index of `other` and broadcast:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">!=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"cost"</span><span class="p">,</span> <span class="s2">"revenue"</span><span class="p">])</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B True False</span> |
| <span class="go">C False True</span> |
| |
| <span class="go">Use the method to control the broadcast axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B True True</span> |
| <span class="go">C True True</span> |
| <span class="go">D True True</span> |
| |
| <span class="go">When comparing to an arbitrary sequence, the number of columns must</span> |
| <span class="go">match the number elements in `other`:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B False False</span> |
| <span class="go">C False False</span> |
| |
| <span class="go">Use the method to control the axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">([</span><span class="mi">250</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B False True</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">Compare to a DataFrame of different shape.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">other</span> |
| <span class="go"> revenue</span> |
| <span class="go">A 300</span> |
| <span class="go">B 250</span> |
| <span class="go">C 100</span> |
| <span class="go">D 150</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False False</span> |
| <span class="go">B False False</span> |
| <span class="go">C False True</span> |
| <span class="go">D False False</span> |
| |
| <span class="go">Compare to a MultiIndex by level.</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multindex</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">220</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">175</span><span class="p">,</span> <span class="mi">225</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[[</span><span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">df_multindex</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A 250 100</span> |
| <span class="go"> B 150 250</span> |
| <span class="go"> C 100 300</span> |
| <span class="go">Q2 A 150 200</span> |
| <span class="go"> B 300 175</span> |
| <span class="go"> C 220 225</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">df_multindex</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A True True</span> |
| <span class="go"> B True True</span> |
| <span class="go"> C True True</span> |
| <span class="go">Q2 A False True</span> |
| <span class="go"> B True False</span> |
| <span class="go"> C True False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.mad"> |
| <code class="descname">mad</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.mad" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.mask"> |
| <code class="descname">mask</code><span class="sig-paren">(</span><em>cond</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.mask" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.melt"> |
| <code class="descname">melt</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.melt" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.mod"> |
| <code class="descname">mod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.mod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.mul"> |
| <code class="descname">mul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.mul" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.multiply"> |
| <code class="descname">multiply</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.multiply" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.ndim"> |
| <code class="descname">ndim</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.ndim" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.ne"> |
| <code class="descname">ne</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.ne" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get Not equal to of dataframe and other, element-wise (binary operator <cite>ne</cite>).</p> |
| <p>Among flexible wrappers (<cite>eq</cite>, <cite>ne</cite>, <cite>le</cite>, <cite>lt</cite>, <cite>ge</cite>, <cite>gt</cite>) to comparison |
| operators.</p> |
| <p>Equivalent to <cite>==</cite>, <cite>!=</cite>, <cite><=</cite>, <cite><</cite>, <cite>>=</cite>, <cite>></cite> with support to choose axis |
| (rows or columns) and level for comparison.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>other</strong> (<em>scalar</em><em>, </em><em>sequence</em><em>, </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries" title="apache_beam.dataframe.frames.DeferredSeries"><em>DeferredSeries</em></a><em>, or </em><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame"><em>DeferredDataFrame</em></a>) – Any single or multiple element data structure, or list-like object.</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 'columns'</em>) – Whether to compare by the index (0 or ‘index’) or columns |
| (1 or ‘columns’).</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>label</em>) – Broadcast across a level, matching Index values on the passed |
| MultiIndex level.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">Result of the comparison.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">DeferredDataFrame of bool</p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.eq" title="apache_beam.dataframe.frames.DeferredDataFrame.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.eq()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ne" title="apache_beam.dataframe.frames.DeferredDataFrame.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ne()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.le" title="apache_beam.dataframe.frames.DeferredDataFrame.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.le()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for less than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.lt" title="apache_beam.dataframe.frames.DeferredDataFrame.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.lt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly less than inequality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.ge" title="apache_beam.dataframe.frames.DeferredDataFrame.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.ge()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for greater than inequality or equality elementwise.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.gt" title="apache_beam.dataframe.frames.DeferredDataFrame.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.gt()</span></code></a></dt> |
| <dd>Compare DeferredDataFrames for strictly greater than inequality elementwise.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Notes</p> |
| <p>Mismatched indices will be unioned together. |
| <cite>NaN</cite> values are considered different (i.e. <cite>NaN</cite> != <cite>NaN</cite>).</p> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A 250 100</span> |
| <span class="go">B 150 250</span> |
| <span class="go">C 100 300</span> |
| |
| <span class="go">Comparison with a scalar, using either the operator or method:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="mi">100</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False True</span> |
| <span class="go">B False False</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">When `other` is a :class:`Series`, the columns of a DataFrame are aligned</span> |
| <span class="go">with the index of `other` and broadcast:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">!=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"cost"</span><span class="p">,</span> <span class="s2">"revenue"</span><span class="p">])</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B True False</span> |
| <span class="go">C False True</span> |
| |
| <span class="go">Use the method to control the broadcast axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">100</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B True True</span> |
| <span class="go">C True True</span> |
| <span class="go">D True True</span> |
| |
| <span class="go">When comparing to an arbitrary sequence, the number of columns must</span> |
| <span class="go">match the number elements in `other`:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">==</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True True</span> |
| <span class="go">B False False</span> |
| <span class="go">C False False</span> |
| |
| <span class="go">Use the method to control the axis:</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">eq</span><span class="p">([</span><span class="mi">250</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A True False</span> |
| <span class="go">B False True</span> |
| <span class="go">C True False</span> |
| |
| <span class="go">Compare to a DataFrame of different shape.</span> |
| |
| <span class="gp">>>> </span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">300</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">other</span> |
| <span class="go"> revenue</span> |
| <span class="go">A 300</span> |
| <span class="go">B 250</span> |
| <span class="go">C 100</span> |
| <span class="go">D 150</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">A False False</span> |
| <span class="go">B False False</span> |
| <span class="go">C False True</span> |
| <span class="go">D False False</span> |
| |
| <span class="go">Compare to a MultiIndex by level.</span> |
| |
| <span class="gp">>>> </span><span class="n">df_multindex</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'cost'</span><span class="p">:</span> <span class="p">[</span><span class="mi">250</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">220</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'revenue'</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">175</span><span class="p">,</span> <span class="mi">225</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[[</span><span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q1'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">,</span> <span class="s1">'Q2'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">df_multindex</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A 250 100</span> |
| <span class="go"> B 150 250</span> |
| <span class="go"> C 100 300</span> |
| <span class="go">Q2 A 150 200</span> |
| <span class="go"> B 300 175</span> |
| <span class="go"> C 220 225</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">df_multindex</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"> cost revenue</span> |
| <span class="go">Q1 A True True</span> |
| <span class="go"> B True True</span> |
| <span class="go"> C True True</span> |
| <span class="go">Q2 A False True</span> |
| <span class="go"> B True False</span> |
| <span class="go"> C True False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.pad"> |
| <code class="descname">pad</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.pad" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.pct_change"> |
| <code class="descname">pct_change</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.pct_change" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.pipe"> |
| <code class="descname">pipe</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.pipe" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.pivot"> |
| <code class="descname">pivot</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.pivot" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.pivot_table"> |
| <code class="descname">pivot_table</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.pivot_table" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.pow"> |
| <code class="descname">pow</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.pow" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.radd"> |
| <code class="descname">radd</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.radd" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rank"> |
| <code class="descname">rank</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rank" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rdiv"> |
| <code class="descname">rdiv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rdiv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.reindex"> |
| <code class="descname">reindex</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.reindex" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.reindex_like"> |
| <code class="descname">reindex_like</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.reindex_like" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.reorder_levels"> |
| <code class="descname">reorder_levels</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.reorder_levels" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Rearrange index levels using input order. May not drop or duplicate levels.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>order</strong> (<em>list of int</em><em> or </em><em>list of str</em>) – List representing new level order. Reference level by number |
| (position) or by key (label).</li> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 0</em>) – Where to reorder levels.</li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"></p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p>This operation has no known divergences from the pandas API.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.resample"> |
| <code class="descname">resample</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.resample" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rfloordiv"> |
| <code class="descname">rfloordiv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rfloordiv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rmod"> |
| <code class="descname">rmod</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rmod" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rmul"> |
| <code class="descname">rmul</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rmul" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rolling"> |
| <code class="descname">rolling</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rolling" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rpow"> |
| <code class="descname">rpow</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rpow" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rsub"> |
| <code class="descname">rsub</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rsub" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.rtruediv"> |
| <code class="descname">rtruediv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.rtruediv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.sample"> |
| <code class="descname">sample</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.sample" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.sem"> |
| <code class="descname">sem</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.sem" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.set_axis"> |
| <code class="descname">set_axis</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.set_axis" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.set_flags"> |
| <code class="descname">set_flags</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.set_flags" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.size"> |
| <code class="descname">size</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.size" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.skew"> |
| <code class="descname">skew</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.skew" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.slice_shift"> |
| <code class="descname">slice_shift</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.slice_shift" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.sort_index"> |
| <code class="descname">sort_index</code><span class="sig-paren">(</span><em>axis</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.sort_index" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sort object by labels (along an axis).</p> |
| <p>Returns a new DataFrame sorted by label if <cite>inplace</cite> argument is |
| <code class="docutils literal notranslate"><span class="pre">False</span></code>, otherwise updates the original DataFrame and returns None.</p> |
| <table class="docutils field-list" frame="void" rules="none"> |
| <col class="field-name" /> |
| <col class="field-body" /> |
| <tbody valign="top"> |
| <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> |
| <li><strong>axis</strong> (<em>{0</em><em> or </em><em>'index'</em><em>, </em><em>1</em><em> or </em><em>'columns'}</em><em>, </em><em>default 0</em>) – The axis along which to sort. The value 0 identifies the rows, |
| and 1 identifies the columns.</li> |
| <li><strong>level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>level name</em><em> or </em><em>list of ints</em><em> or </em><em>list of level names</em>) – If not None, sort on values in specified index level(s).</li> |
| <li><strong>ascending</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em> or </em><em>list-like of bools</em><em>, </em><em>default True</em>) – Sort ascending vs. descending. When the index is a MultiIndex the |
| sort direction can be controlled for each level individually.</li> |
| <li><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default False</em>) – If True, perform operation in-place.</li> |
| <li><strong>kind</strong> (<em>{'quicksort'</em><em>, </em><em>'mergesort'</em><em>, </em><em>'heapsort'}</em><em>, </em><em>default 'quicksort'</em>) – Choice of sorting algorithm. See also ndarray.np.sort for more |
| information. <cite>mergesort</cite> is the only stable algorithm. For |
| DeferredDataFrames, this option is only applied when sorting on a single |
| column or label.</li> |
| <li><strong>na_position</strong> (<em>{'first'</em><em>, </em><em>'last'}</em><em>, </em><em>default 'last'</em>) – Puts NaNs at the beginning if <cite>first</cite>; <cite>last</cite> puts NaNs at the end. |
| Not implemented for MultiIndex.</li> |
| <li><strong>sort_remaining</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default True</em>) – If True and sorting by level and index is multilevel, sort by other |
| levels too (in order) after sorting by specified level.</li> |
| <li><strong>ignore_index</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>default False</em>) – <p>If True, the resulting axis will be labeled 0, 1, …, n - 1.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified">New in version 1.0.0.</span></p> |
| </div> |
| </li> |
| <li><strong>key</strong> (<em>callable</em><em>, </em><em>optional</em>) – <p>If not None, apply the key function to the index values |
| before sorting. This is similar to the <cite>key</cite> argument in the |
| builtin <code class="xref py py-meth docutils literal notranslate"><span class="pre">sorted()</span></code> function, with the notable difference that |
| this <cite>key</cite> function should be <em>vectorized</em>. It should expect an |
| <code class="docutils literal notranslate"><span class="pre">Index</span></code> and return an <code class="docutils literal notranslate"><span class="pre">Index</span></code> of the same shape. For MultiIndex |
| inputs, the key is applied <em>per level</em>.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified">New in version 1.1.0.</span></p> |
| </div> |
| </li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The original DeferredDataFrame sorted by the labels or None if <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code>.</p> |
| </td> |
| </tr> |
| <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame" title="apache_beam.dataframe.frames.DeferredDataFrame">DeferredDataFrame</a> or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)">None</a></p> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Differences from pandas</p> |
| <p><code class="docutils literal notranslate"><span class="pre">axis=index</span></code> is not allowed because it imposes an ordering on the |
| dataset, and we cannot guarantee it will be maintained (see |
| <a class="reference external" href="https://s.apache.org/dataframe-order-sensitive-operations">https://s.apache.org/dataframe-order-sensitive-operations</a>). Only |
| <code class="docutils literal notranslate"><span class="pre">axis=columns</span></code> is allowed.</p> |
| <div class="admonition seealso"> |
| <p class="first admonition-title">See also</p> |
| <dl class="last docutils"> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.sort_index" title="apache_beam.dataframe.frames.DeferredSeries.sort_index"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.sort_index()</span></code></a></dt> |
| <dd>Sort DeferredSeries by the index.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredDataFrame.sort_values" title="apache_beam.dataframe.frames.DeferredDataFrame.sort_values"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredDataFrame.sort_values()</span></code></a></dt> |
| <dd>Sort DeferredDataFrame by the value.</dd> |
| <dt><a class="reference internal" href="#apache_beam.dataframe.frames.DeferredSeries.sort_values" title="apache_beam.dataframe.frames.DeferredSeries.sort_values"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DeferredSeries.sort_values()</span></code></a></dt> |
| <dd>Sort DeferredSeries by the value.</dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p><strong>NOTE:</strong> These examples are pulled directly from the pandas documentation for convenience. Usage of the Beam DataFrame API will look different because it is a deferred API. In addition, some arguments shown here may not be supported, see <strong>‘Differences from pandas’</strong> for details.</p> |
| <div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">29</span><span class="p">,</span> <span class="mi">234</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">150</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span> |
| <span class="go"> A</span> |
| <span class="go">1 4</span> |
| <span class="go">29 2</span> |
| <span class="go">100 1</span> |
| <span class="go">150 5</span> |
| <span class="go">234 3</span> |
| |
| <span class="go">By default, it sorts in ascending order, to sort in descending order,</span> |
| <span class="go">use ``ascending=False``</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="go"> A</span> |
| <span class="go">234 3</span> |
| <span class="go">150 5</span> |
| <span class="go">100 1</span> |
| <span class="go">29 2</span> |
| <span class="go">1 4</span> |
| |
| <span class="go">A key function can be specified which is applied to the index before</span> |
| <span class="go">sorting. For a ``MultiIndex`` this is applied to each level separately.</span> |
| |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">())</span> |
| <span class="go"> a</span> |
| <span class="go">A 1</span> |
| <span class="go">b 2</span> |
| <span class="go">C 3</span> |
| <span class="go">d 4</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.sort_values"> |
| <code class="descname">sort_values</code><span class="sig-paren">(</span><em>axis</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.sort_values" title="Permalink to this definition">¶</a></dt> |
| <dd><p><code class="docutils literal notranslate"><span class="pre">sort_values</span></code> is not implemented.</p> |
| <p>It is not implemented for <code class="docutils literal notranslate"><span class="pre">axis=index</span></code> because it imposes an ordering on |
| the dataset, and we cannot guarantee it will be maintained (see |
| <a class="reference external" href="https://s.apache.org/dataframe-order-sensitive-operations">https://s.apache.org/dataframe-order-sensitive-operations</a>).</p> |
| <p>It is not implemented for <code class="docutils literal notranslate"><span class="pre">axis=columns</span></code> because it makes the order of |
| the columns depend on the data (see |
| <a class="reference external" href="https://s.apache.org/dataframe-non-deferred-column-names">https://s.apache.org/dataframe-non-deferred-column-names</a>).</p> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.sparse"> |
| <code class="descname">sparse</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.sparse" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.squeeze"> |
| <code class="descname">squeeze</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.squeeze" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.std"> |
| <code class="descname">std</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.std" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.style"> |
| <code class="descname">style</code><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.style" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.sub"> |
| <code class="descname">sub</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.sub" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.subtract"> |
| <code class="descname">subtract</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.subtract" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.swapaxes"> |
| <code class="descname">swapaxes</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.swapaxes" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.swaplevel"> |
| <code class="descname">swaplevel</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.swaplevel" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_clipboard"> |
| <code class="descname">to_clipboard</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_clipboard" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_csv"> |
| <code class="descname">to_csv</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_csv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_excel"> |
| <code class="descname">to_excel</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_excel" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_feather"> |
| <code class="descname">to_feather</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_feather" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_gbq"> |
| <code class="descname">to_gbq</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_gbq" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_hdf"> |
| <code class="descname">to_hdf</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_hdf" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_hdf is not supported in |
| the Beam DataFrame API because HDF5 is a random access file format.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_html"> |
| <code class="descname">to_html</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_html" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_json"> |
| <code class="descname">to_json</code><span class="sig-paren">(</span><em>path</em>, <em>orient=None</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_json" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_latex"> |
| <code class="descname">to_latex</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_latex" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_markdown"> |
| <code class="descname">to_markdown</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_markdown" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_msgpack"> |
| <code class="descname">to_msgpack</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_msgpack" title="Permalink to this definition">¶</a></dt> |
| <dd><p>pandas.DataFrame.to_msgpack is not supported in |
| the Beam DataFrame API because it is deprecated in pandas.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_parquet"> |
| <code class="descname">to_parquet</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_parquet" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_period"> |
| <code class="descname">to_period</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_period" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_pickle"> |
| <code class="descname">to_pickle</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_pickle" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_sql"> |
| <code class="descname">to_sql</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_sql" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_stata"> |
| <code class="descname">to_stata</code><span class="sig-paren">(</span><em>path</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_stata" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_timestamp"> |
| <code class="descname">to_timestamp</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_timestamp" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.to_xarray"> |
| <code class="descname">to_xarray</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.to_xarray" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.transform"> |
| <code class="descname">transform</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.transform" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.truediv"> |
| <code class="descname">truediv</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.truediv" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.truncate"> |
| <code class="descname">truncate</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.truncate" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.tshift"> |
| <code class="descname">tshift</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.tshift" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.tz_convert"> |
| <code class="descname">tz_convert</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.tz_convert" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.tz_localize"> |
| <code class="descname">tz_localize</code><span class="sig-paren">(</span><em>ambiguous</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.tz_localize" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.value_counts"> |
| <code class="descname">value_counts</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.value_counts" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.var"> |
| <code class="descname">var</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.var" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.where"> |
| <code class="descname">where</code><span class="sig-paren">(</span><em>cond</em>, <em>other</em>, <em>errors</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.where" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="classmethod"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.wrap"> |
| <em class="property">classmethod </em><code class="descname">wrap</code><span class="sig-paren">(</span><em>expr</em>, <em>split_tuples=True</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.wrap" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.dataframe.frames.DeferredDataFrame.xs"> |
| <code class="descname">xs</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.dataframe.frames.DeferredDataFrame.xs" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
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