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<h1>Source code for pyspark.sql.group</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span><span class="p">,</span> <span class="n">overload</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">py4j.java_gateway</span> <span class="kn">import</span> <span class="n">JavaObject</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.column</span> <span class="kn">import</span> <span class="n">Column</span><span class="p">,</span> <span class="n">_to_seq</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.session</span> <span class="kn">import</span> <span class="n">SparkSession</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.dataframe</span> <span class="kn">import</span> <span class="n">DataFrame</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.pandas.group_ops</span> <span class="kn">import</span> <span class="n">PandasGroupedOpsMixin</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="n">StructType</span><span class="p">,</span> <span class="n">StructField</span><span class="p">,</span> <span class="n">IntegerType</span><span class="p">,</span> <span class="n">StringType</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">pyspark.sql._typing</span> <span class="kn">import</span> <span class="n">LiteralType</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;GroupedData&quot;</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">dfapi</span><span class="p">(</span><span class="n">f</span><span class="p">:</span> <span class="n">Callable</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">_api</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="s2">&quot;GroupedData&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__name__</span>
<span class="n">jdf</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jgd</span><span class="p">,</span> <span class="n">name</span><span class="p">)()</span>
<span class="k">return</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">jdf</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="p">)</span>
<span class="n">_api</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__name__</span>
<span class="n">_api</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__doc__</span>
<span class="k">return</span> <span class="n">_api</span>
<span class="k">def</span> <span class="nf">df_varargs_api</span><span class="p">(</span><span class="n">f</span><span class="p">:</span> <span class="n">Callable</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">_api</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="s2">&quot;GroupedData&quot;</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__name__</span>
<span class="n">jdf</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jgd</span><span class="p">,</span> <span class="n">name</span><span class="p">)(</span><span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
<span class="k">return</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">jdf</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="p">)</span>
<span class="n">_api</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__name__</span>
<span class="n">_api</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="vm">__doc__</span>
<span class="k">return</span> <span class="n">_api</span>
<div class="viewcode-block" id="GroupedData"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.html#pyspark.sql.GroupedData">[docs]</a><span class="k">class</span> <span class="nc">GroupedData</span><span class="p">(</span><span class="n">PandasGroupedOpsMixin</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A set of methods for aggregations on a :class:`DataFrame`,</span>
<span class="sd"> created by :func:`DataFrame.groupBy`.</span>
<span class="sd"> .. versionadded:: 1.3</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">jgd</span><span class="p">:</span> <span class="n">JavaObject</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jgd</span> <span class="o">=</span> <span class="n">jgd</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_df</span> <span class="o">=</span> <span class="n">df</span>
<span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="p">:</span> <span class="n">SparkSession</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">sparkSession</span>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">agg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">exprs</span><span class="p">:</span> <span class="n">Column</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="o">...</span>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">agg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">__exprs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="o">...</span>
<div class="viewcode-block" id="GroupedData.agg"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.agg.html#pyspark.sql.GroupedData.agg">[docs]</a> <span class="k">def</span> <span class="nf">agg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">exprs</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Column</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Compute aggregates and returns the result as a :class:`DataFrame`.</span>
<span class="sd"> The available aggregate functions can be:</span>
<span class="sd"> 1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`, `count`</span>
<span class="sd"> 2. group aggregate pandas UDFs, created with :func:`pyspark.sql.functions.pandas_udf`</span>
<span class="sd"> .. note:: There is no partial aggregation with group aggregate UDFs, i.e.,</span>
<span class="sd"> a full shuffle is required. Also, all the data of a group will be loaded into</span>
<span class="sd"> memory, so the user should be aware of the potential OOM risk if data is skewed</span>
<span class="sd"> and certain groups are too large to fit in memory.</span>
<span class="sd"> .. seealso:: :func:`pyspark.sql.functions.pandas_udf`</span>
<span class="sd"> If ``exprs`` is a single :class:`dict` mapping from string to string, then the key</span>
<span class="sd"> is the column to perform aggregation on, and the value is the aggregate function.</span>
<span class="sd"> Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> exprs : dict</span>
<span class="sd"> a dict mapping from column name (string) to aggregate functions (string),</span>
<span class="sd"> or a list of :class:`Column`.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Built-in aggregation functions and group aggregate pandas UDFs cannot be mixed</span>
<span class="sd"> in a single call to this function.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; gdf = df.groupBy(df.name)</span>
<span class="sd"> &gt;&gt;&gt; sorted(gdf.agg({&quot;*&quot;: &quot;count&quot;}).collect())</span>
<span class="sd"> [Row(name=&#39;Alice&#39;, count(1)=1), Row(name=&#39;Bob&#39;, count(1)=1)]</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.sql import functions as F</span>
<span class="sd"> &gt;&gt;&gt; sorted(gdf.agg(F.min(df.age)).collect())</span>
<span class="sd"> [Row(name=&#39;Alice&#39;, min(age)=2), Row(name=&#39;Bob&#39;, min(age)=5)]</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.sql.functions import pandas_udf, PandasUDFType</span>
<span class="sd"> &gt;&gt;&gt; @pandas_udf(&#39;int&#39;, PandasUDFType.GROUPED_AGG) # doctest: +SKIP</span>
<span class="sd"> ... def min_udf(v):</span>
<span class="sd"> ... return v.min()</span>
<span class="sd"> &gt;&gt;&gt; sorted(gdf.agg(min_udf(df.age)).collect()) # doctest: +SKIP</span>
<span class="sd"> [Row(name=&#39;Alice&#39;, min_udf(age)=2), Row(name=&#39;Bob&#39;, min_udf(age)=5)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">exprs</span><span class="p">,</span> <span class="s2">&quot;exprs should not be empty&quot;</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">exprs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">exprs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">dict</span><span class="p">):</span>
<span class="n">jdf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jgd</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="n">exprs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Columns</span>
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">Column</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">exprs</span><span class="p">),</span> <span class="s2">&quot;all exprs should be Column&quot;</span>
<span class="n">exprs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Column</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">exprs</span><span class="p">)</span>
<span class="n">jdf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jgd</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="n">exprs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">_jc</span><span class="p">,</span> <span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="p">[</span><span class="n">c</span><span class="o">.</span><span class="n">_jc</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">exprs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]]))</span>
<span class="k">return</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">jdf</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="p">)</span></div>
<div class="viewcode-block" id="GroupedData.count"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.count.html#pyspark.sql.GroupedData.count">[docs]</a> <span class="nd">@dfapi</span>
<span class="k">def</span> <span class="nf">count</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Counts the number of records for each group.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; sorted(df.groupBy(df.age).count().collect())</span>
<span class="sd"> [Row(age=2, count=1), Row(age=5, count=1)]</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
<div class="viewcode-block" id="GroupedData.mean"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.mean.html#pyspark.sql.GroupedData.mean">[docs]</a> <span class="nd">@df_varargs_api</span>
<span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Computes average values for each numeric columns for each group.</span>
<span class="sd"> :func:`mean` is an alias for :func:`avg`.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cols : str</span>
<span class="sd"> column names. Non-numeric columns are ignored.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.groupBy().mean(&#39;age&#39;).collect()</span>
<span class="sd"> [Row(avg(age)=3.5)]</span>
<span class="sd"> &gt;&gt;&gt; df3.groupBy().mean(&#39;age&#39;, &#39;height&#39;).collect()</span>
<span class="sd"> [Row(avg(age)=3.5, avg(height)=82.5)]</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
<div class="viewcode-block" id="GroupedData.avg"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.avg.html#pyspark.sql.GroupedData.avg">[docs]</a> <span class="nd">@df_varargs_api</span>
<span class="k">def</span> <span class="nf">avg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Computes average values for each numeric columns for each group.</span>
<span class="sd"> :func:`mean` is an alias for :func:`avg`.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cols : str</span>
<span class="sd"> column names. Non-numeric columns are ignored.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.groupBy().avg(&#39;age&#39;).collect()</span>
<span class="sd"> [Row(avg(age)=3.5)]</span>
<span class="sd"> &gt;&gt;&gt; df3.groupBy().avg(&#39;age&#39;, &#39;height&#39;).collect()</span>
<span class="sd"> [Row(avg(age)=3.5, avg(height)=82.5)]</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
<div class="viewcode-block" id="GroupedData.max"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.max.html#pyspark.sql.GroupedData.max">[docs]</a> <span class="nd">@df_varargs_api</span>
<span class="k">def</span> <span class="nf">max</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Computes the max value for each numeric columns for each group.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.groupBy().max(&#39;age&#39;).collect()</span>
<span class="sd"> [Row(max(age)=5)]</span>
<span class="sd"> &gt;&gt;&gt; df3.groupBy().max(&#39;age&#39;, &#39;height&#39;).collect()</span>
<span class="sd"> [Row(max(age)=5, max(height)=85)]</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
<div class="viewcode-block" id="GroupedData.min"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.min.html#pyspark.sql.GroupedData.min">[docs]</a> <span class="nd">@df_varargs_api</span>
<span class="k">def</span> <span class="nf">min</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Computes the min value for each numeric column for each group.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cols : str</span>
<span class="sd"> column names. Non-numeric columns are ignored.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.groupBy().min(&#39;age&#39;).collect()</span>
<span class="sd"> [Row(min(age)=2)]</span>
<span class="sd"> &gt;&gt;&gt; df3.groupBy().min(&#39;age&#39;, &#39;height&#39;).collect()</span>
<span class="sd"> [Row(min(age)=2, min(height)=80)]</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
<div class="viewcode-block" id="GroupedData.sum"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.sum.html#pyspark.sql.GroupedData.sum">[docs]</a> <span class="nd">@df_varargs_api</span>
<span class="k">def</span> <span class="nf">sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;Computes the sum for each numeric columns for each group.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cols : str</span>
<span class="sd"> column names. Non-numeric columns are ignored.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.groupBy().sum(&#39;age&#39;).collect()</span>
<span class="sd"> [Row(sum(age)=7)]</span>
<span class="sd"> &gt;&gt;&gt; df3.groupBy().sum(&#39;age&#39;, &#39;height&#39;).collect()</span>
<span class="sd"> [Row(sum(age)=7, sum(height)=165)]</span>
<span class="sd"> &quot;&quot;&quot;</span></div>
<div class="viewcode-block" id="GroupedData.pivot"><a class="viewcode-back" href="../../../reference/pyspark.sql/api/pyspark.sql.GroupedData.pivot.html#pyspark.sql.GroupedData.pivot">[docs]</a> <span class="k">def</span> <span class="nf">pivot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pivot_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">values</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="s2">&quot;LiteralType&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;GroupedData&quot;</span><span class="p">:</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.</span>
<span class="sd"> There are two versions of pivot function: one that requires the caller to specify the list</span>
<span class="sd"> of distinct values to pivot on, and one that does not. The latter is more concise but less</span>
<span class="sd"> efficient, because Spark needs to first compute the list of distinct values internally.</span>
<span class="sd"> .. versionadded:: 1.6.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> pivot_col : str</span>
<span class="sd"> Name of the column to pivot.</span>
<span class="sd"> values : list, optional</span>
<span class="sd"> List of values that will be translated to columns in the output DataFrame.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> # Compute the sum of earnings for each year by course with each course as a separate column</span>
<span class="sd"> &gt;&gt;&gt; df4.groupBy(&quot;year&quot;).pivot(&quot;course&quot;, [&quot;dotNET&quot;, &quot;Java&quot;]).sum(&quot;earnings&quot;).collect()</span>
<span class="sd"> [Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)]</span>
<span class="sd"> # Or without specifying column values (less efficient)</span>
<span class="sd"> &gt;&gt;&gt; df4.groupBy(&quot;year&quot;).pivot(&quot;course&quot;).sum(&quot;earnings&quot;).collect()</span>
<span class="sd"> [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]</span>
<span class="sd"> &gt;&gt;&gt; df5.groupBy(&quot;sales.year&quot;).pivot(&quot;sales.course&quot;).sum(&quot;sales.earnings&quot;).collect()</span>
<span class="sd"> [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">values</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">jgd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jgd</span><span class="o">.</span><span class="n">pivot</span><span class="p">(</span><span class="n">pivot_col</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">jgd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jgd</span><span class="o">.</span><span class="n">pivot</span><span class="p">(</span><span class="n">pivot_col</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span>
<span class="k">return</span> <span class="n">GroupedData</span><span class="p">(</span><span class="n">jgd</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">)</span></div></div>
<span class="k">def</span> <span class="nf">_test</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">doctest</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">Row</span><span class="p">,</span> <span class="n">SparkSession</span>
<span class="kn">import</span> <span class="nn">pyspark.sql.group</span>
<span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">group</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">&quot;local[4]&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">&quot;sql.group tests&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sparkContext</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;sc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;spark&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;df&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;Alice&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s2">&quot;Bob&quot;</span><span class="p">)])</span><span class="o">.</span><span class="n">toDF</span><span class="p">(</span>
<span class="n">StructType</span><span class="p">([</span><span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">IntegerType</span><span class="p">()),</span> <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="n">StringType</span><span class="p">())])</span>
<span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;df3&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span>
<span class="p">[</span><span class="n">Row</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Alice&quot;</span><span class="p">,</span> <span class="n">age</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">80</span><span class="p">),</span> <span class="n">Row</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Bob&quot;</span><span class="p">,</span> <span class="n">age</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">85</span><span class="p">)]</span>
<span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;df4&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;dotNET&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2012</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">10000</span><span class="p">),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;Java&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2012</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">20000</span><span class="p">),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;dotNET&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2012</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">5000</span><span class="p">),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;dotNET&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2013</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">48000</span><span class="p">),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;Java&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2013</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">30000</span><span class="p">),</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;df5&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">Row</span><span class="p">(</span><span class="n">training</span><span class="o">=</span><span class="s2">&quot;expert&quot;</span><span class="p">,</span> <span class="n">sales</span><span class="o">=</span><span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;dotNET&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2012</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">10000</span><span class="p">)),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">training</span><span class="o">=</span><span class="s2">&quot;junior&quot;</span><span class="p">,</span> <span class="n">sales</span><span class="o">=</span><span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;Java&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2012</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">20000</span><span class="p">)),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">training</span><span class="o">=</span><span class="s2">&quot;expert&quot;</span><span class="p">,</span> <span class="n">sales</span><span class="o">=</span><span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;dotNET&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2012</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">5000</span><span class="p">)),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">training</span><span class="o">=</span><span class="s2">&quot;junior&quot;</span><span class="p">,</span> <span class="n">sales</span><span class="o">=</span><span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;dotNET&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2013</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">48000</span><span class="p">)),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">training</span><span class="o">=</span><span class="s2">&quot;expert&quot;</span><span class="p">,</span> <span class="n">sales</span><span class="o">=</span><span class="n">Row</span><span class="p">(</span><span class="n">course</span><span class="o">=</span><span class="s2">&quot;Java&quot;</span><span class="p">,</span> <span class="n">year</span><span class="o">=</span><span class="mi">2013</span><span class="p">,</span> <span class="n">earnings</span><span class="o">=</span><span class="mi">30000</span><span class="p">)),</span>
<span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span>
<span class="n">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">group</span><span class="p">,</span>
<span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span>
<span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span> <span class="o">|</span> <span class="n">doctest</span><span class="o">.</span><span class="n">NORMALIZE_WHITESPACE</span> <span class="o">|</span> <span class="n">doctest</span><span class="o">.</span><span class="n">REPORT_NDIFF</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="n">_test</span><span class="p">()</span>
</pre></div>
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