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| <div class="section" id="module-apache_beam.dataframe.convert"> |
| <span id="apache-beam-dataframe-convert-module"></span><h1>apache_beam.dataframe.convert module<a class="headerlink" href="#module-apache_beam.dataframe.convert" title="Permalink to this headline">¶</a></h1> |
| <dl class="function"> |
| <dt id="apache_beam.dataframe.convert.to_dataframe"> |
| <code class="descclassname">apache_beam.dataframe.convert.</code><code class="descname">to_dataframe</code><span class="sig-paren">(</span><em>pcoll</em>, <em>proxy=None</em>, <em>label=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/convert.html#to_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.convert.to_dataframe" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Converts a PCollection to a deferred dataframe-like object, which can |
| manipulated with pandas methods like <cite>filter</cite> and <cite>groupby</cite>.</p> |
| <p>For example, one might write:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">=</span> <span class="o">...</span> |
| <span class="n">df</span> <span class="o">=</span> <span class="n">to_dataframe</span><span class="p">(</span><span class="n">pcoll</span><span class="p">,</span> <span class="n">proxy</span><span class="o">=...</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'col'</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">pcoll_result</span> <span class="o">=</span> <span class="n">to_pcollection</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>A proxy object must be given if the schema for the PCollection is not known.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="apache_beam.dataframe.convert.to_pcollection"> |
| <code class="descclassname">apache_beam.dataframe.convert.</code><code class="descname">to_pcollection</code><span class="sig-paren">(</span><em>*dataframes</em>, <em>label=None</em>, <em>always_return_tuple=False</em>, <em>yield_elements='schemas'</em>, <em>include_indexes=False</em>, <em>pipeline=None</em><span class="sig-paren">)</span> → Union[apache_beam.pvalue.PCollection, Tuple[apache_beam.pvalue.PCollection, ...]]<a class="reference internal" href="_modules/apache_beam/dataframe/convert.html#to_pcollection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.convert.to_pcollection" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Converts one or more deferred dataframe-like objects back to a PCollection.</p> |
| <p>This method creates and applies the actual Beam operations that compute |
| the given deferred dataframes, returning a PCollection of their results. By |
| default the resulting PCollections are schema-aware PCollections where each |
| element is one row from the output dataframes, excluding indexes. This |
| behavior can be modified with the <cite>yield_elements</cite> and <cite>include_indexes</cite> |
| arguments.</p> |
| <p>Also accepts non-deferred pandas dataframes, which are converted to deferred, |
| schema’d PCollections. In this case the contents of the entire dataframe are |
| serialized into the graph, so for large amounts of data it is preferable to |
| write them to disk and read them with one of the read methods.</p> |
| <p>If more than one (related) result is desired, it can be more efficient to |
| pass them all at the same time to this method.</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 last simple"> |
| <li><strong>label</strong> – (optional, default “ToPCollection(…)””) the label to use for the |
| conversion transform.</li> |
| <li><strong>always_return_tuple</strong> – (optional, default: False) If true, always return |
| a tuple of PCollections, even if there’s only one output.</li> |
| <li><strong>yield_elements</strong> – (optional, default: “schemas”) If set to “pandas”, return |
| PCollections containing the raw Pandas objects (DataFrames or Series), |
| if set to “schemas”, return an element-wise PCollection, where DataFrame |
| and Series instances are expanded to one element per row. DataFrames are |
| converted to schema-aware PCollections, where column values can be |
| accessed by attribute.</li> |
| <li><strong>include_indexes</strong> – (optional, default: False) When yield_elements=”schemas”, |
| if include_indexes=True, attempt to include index columns in the output |
| schema for expanded DataFrames. Raises an error if any of the index |
| levels are unnamed (name=None), or if any of the names are not unique |
| among all column and index names.</li> |
| <li><strong>pipeline</strong> – (optional, unless non-deferred dataframes are passed) Used when |
| creating a PCollection from a non-deferred dataframe.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
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