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| <div class="section" id="pyspark-pandas-series-groupby"> |
| <h1>pyspark.pandas.Series.groupby<a class="headerlink" href="#pyspark-pandas-series-groupby" title="Permalink to this headline">¶</a></h1> |
| <dl class="py method"> |
| <dt id="pyspark.pandas.Series.groupby"> |
| <code class="sig-prename descclassname">Series.</code><code class="sig-name descname">groupby</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">by</span><span class="p">:</span> <span class="n">Union[Any, Tuple[Any, …], Series, List[Union[Any, Tuple[Any, …], Series]]]</span></em>, <em class="sig-param"><span class="n">axis</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>int<span class="p">, </span>str<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">0</span></em>, <em class="sig-param"><span class="n">as_index</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">True</span></em>, <em class="sig-param"><span class="n">dropna</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">True</span></em><span class="sig-paren">)</span> → SeriesGroupBy<a class="reference internal" href="../../../_modules/pyspark/pandas/series.html#Series.groupby"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.pandas.Series.groupby" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Group DataFrame or Series using one or more columns.</p> |
| <p>A groupby operation involves some combination of splitting the |
| object, applying a function, and combining the results. This can be |
| used to group large amounts of data and compute operations on these |
| groups.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>by</strong><span class="classifier">Series, label, or list of labels</span></dt><dd><p>Used to determine the groups for the groupby. |
| If Series is passed, the Series or dict VALUES |
| will be used to determine the groups. A label or list of |
| labels may be passed to group by the columns in <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p> |
| </dd> |
| <dt><strong>axis</strong><span class="classifier">int, default 0 or ‘index’</span></dt><dd><p>Can only be set to 0 at the moment.</p> |
| </dd> |
| <dt><strong>as_index</strong><span class="classifier">bool, default True</span></dt><dd><p>For aggregated output, return object with group labels as the |
| index. Only relevant for DataFrame input. as_index=False is |
| effectively “SQL-style” grouped output.</p> |
| </dd> |
| <dt><strong>dropna</strong><span class="classifier">bool, default True</span></dt><dd><p>If True, and if group keys contain NA values, |
| NA values together with row/column will be dropped. |
| If False, NA values will also be treated as the key in groups.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt>DataFrameGroupBy or SeriesGroupBy</dt><dd><p>Depends on the calling object and returns groupby object that |
| contains information about the groups.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyspark.pandas.groupby.GroupBy</span></code></dt><dd></dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'Animal'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'Falcon'</span><span class="p">,</span> <span class="s1">'Falcon'</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="s1">'Parrot'</span><span class="p">,</span> <span class="s1">'Parrot'</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'Max Speed'</span><span class="p">:</span> <span class="p">[</span><span class="mf">380.</span><span class="p">,</span> <span class="mf">370.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">,</span> <span class="mf">26.</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">'Animal'</span><span class="p">,</span> <span class="s1">'Max Speed'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> Animal Max Speed</span> |
| <span class="go">0 Falcon 380.0</span> |
| <span class="go">1 Falcon 370.0</span> |
| <span class="go">2 Parrot 24.0</span> |
| <span class="go">3 Parrot 26.0</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Animal'</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span> |
| <span class="go"> Max Speed</span> |
| <span class="go">Animal</span> |
| <span class="go">Falcon 375.0</span> |
| <span class="go">Parrot 25.0</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Animal'</span><span class="p">],</span> <span class="n">as_index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'Animal'</span><span class="p">)</span> |
| <span class="gp">... </span> |
| <span class="go"> Animal Max Speed</span> |
| <span class="go">...Falcon 375.0</span> |
| <span class="go">...Parrot 25.0</span> |
| </pre></div> |
| </div> |
| <p>We can also choose to include NA in group keys or not by setting dropna parameter, |
| the default setting is True:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">l</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="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</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="mi">3</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">2</span><span class="p">]]</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">columns</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="s2">"c"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">"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">sort_index</span><span class="p">()</span> |
| <span class="go"> a c</span> |
| <span class="go">b</span> |
| <span class="go">1.0 2 3</span> |
| <span class="go">2.0 2 5</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">"b"</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="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span> |
| <span class="go"> a c</span> |
| <span class="go">b</span> |
| <span class="go">1.0 2 3</span> |
| <span class="go">2.0 2 5</span> |
| <span class="go">NaN 1 4</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
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