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   <section id="pyspark-sql-groupeddata-applyinpandas">
 <h1>pyspark.sql.GroupedData.applyInPandas<a class="headerlink" href="#pyspark-sql-groupeddata-applyinpandas" title="Permalink to this headline">#</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="pyspark.sql.GroupedData.applyInPandas">
 <span class="sig-prename descclassname"><span class="pre">GroupedData.</span></span><span class="sig-name descname"><span class="pre">applyInPandas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyspark.sql.GroupedData.applyInPandas" title="Permalink to this definition">#</a></dt>
 <dd><p>Maps each group of the current <a class="reference internal" href="pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a> using a pandas udf and returns the result
 as a <cite>DataFrame</cite>.</p>
 <p>The function should take a <cite>pandas.DataFrame</cite> and return another
 <cite>pandas.DataFrame</cite>. Alternatively, the user can pass a function that takes
 a tuple of the grouping key(s) and a <cite>pandas.DataFrame</cite>.
 For each group, all columns are passed together as a <cite>pandas.DataFrame</cite>
 to the user-function and the returned <cite>pandas.DataFrame</cite> are combined as a
 <a class="reference internal" href="pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a>.</p>
 <p>The <cite>schema</cite> should be a <code class="xref py py-class docutils literal notranslate"><span class="pre">StructType</span></code> describing the schema of the returned
 <cite>pandas.DataFrame</cite>. The column labels of the returned <cite>pandas.DataFrame</cite> must either match
 the field names in the defined schema if specified as strings, or match the
 field data types by position if not strings, e.g. integer indices.
 The length of the returned <cite>pandas.DataFrame</cite> can be arbitrary.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">New in version 3.0.0.</span></p>
 </div>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 3.4.0: </span>Support Spark Connect.</p>
 </div>
 <dl class="field-list">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><dl>
 <dt><strong>func</strong><span class="classifier">function</span></dt><dd><p>a Python native function that takes a <cite>pandas.DataFrame</cite> and outputs a
 <cite>pandas.DataFrame</cite>, or that takes one tuple (grouping keys) and a
 <cite>pandas.DataFrame</cite> and outputs a <cite>pandas.DataFrame</cite>.</p>
 </dd>
 <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyspark.sql.types.DataType.html#pyspark.sql.types.DataType" title="pyspark.sql.types.DataType"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.types.DataType</span></code></a> or str</span></dt><dd><p>the return type of the <cite>func</cite> in PySpark. The value can be either a
 <a class="reference internal" href="pyspark.sql.types.DataType.html#pyspark.sql.types.DataType" title="pyspark.sql.types.DataType"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.sql.types.DataType</span></code></a> object or a DDL-formatted type string.</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <dl class="simple">
 <dt><a class="reference internal" href="pyspark.sql.functions.pandas_udf.html#pyspark.sql.functions.pandas_udf" title="pyspark.sql.functions.pandas_udf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyspark.sql.functions.pandas_udf</span></code></a></dt><dd></dd>
 </dl>
 </div>
 <p class="rubric">Notes</p>
 <p>This function requires a full shuffle. All the data of a group will be loaded
 into memory, so the user should be aware of the potential OOM risk if data is skewed
 and certain groups are too large to fit in memory.</p>
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">ceil</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)],</span>
 <span class="gp">... </span>    <span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;v&quot;</span><span class="p">))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">pdf</span><span class="p">):</span>
 <span class="gp">... </span>    <span class="n">v</span> <span class="o">=</span> <span class="n">pdf</span><span class="o">.</span><span class="n">v</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">pdf</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">v</span><span class="o">=</span><span class="p">(</span><span class="n">v</span> <span class="o">-</span> <span class="n">v</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">/</span> <span class="n">v</span><span class="o">.</span><span class="n">std</span><span class="p">())</span>
 <span class="gp">...</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">applyInPandas</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">normalize</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="s2">&quot;id long, v double&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="go">+---+-------------------+</span>
 <span class="go">| id|                  v|</span>
 <span class="go">+---+-------------------+</span>
 <span class="go">|  1|-0.7071067811865475|</span>
 <span class="go">|  1| 0.7071067811865475|</span>
 <span class="go">|  2|-0.8320502943378437|</span>
 <span class="go">|  2|-0.2773500981126146|</span>
 <span class="go">|  2| 1.1094003924504583|</span>
 <span class="go">+---+-------------------+</span>
 </pre></div>
 </div>
 <p>Alternatively, the user can pass a function that takes two arguments.
 In this case, the grouping key(s) will be passed as the first argument and the data will
 be passed as the second argument. The grouping key(s) will be passed as a tuple of numpy
 data types, e.g., <cite>numpy.int32</cite> and <cite>numpy.float64</cite>. The data will still be passed in
 as a <cite>pandas.DataFrame</cite> containing all columns from the original Spark DataFrame.
 This is useful when the user does not want to hardcode grouping key(s) in the function.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)],</span>
 <span class="gp">... </span>    <span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;v&quot;</span><span class="p">))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mean_func</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">pdf</span><span class="p">):</span>
 <span class="gp">... </span>    <span class="c1"># key is a tuple of one numpy.int64, which is the value</span>
 <span class="gp">... </span>    <span class="c1"># of &#39;id&#39; for the current group</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">key</span> <span class="o">+</span> <span class="p">(</span><span class="n">pdf</span><span class="o">.</span><span class="n">v</span><span class="o">.</span><span class="n">mean</span><span class="p">(),)])</span>
 <span class="gp">...</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">applyInPandas</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">mean_func</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="s2">&quot;id long, v double&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="go">+---+---+</span>
 <span class="go">| id|  v|</span>
 <span class="go">+---+---+</span>
 <span class="go">|  1|1.5|</span>
 <span class="go">|  2|6.0|</span>
 <span class="go">+---+---+</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">sum_func</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">pdf</span><span class="p">):</span>
 <span class="gp">... </span>    <span class="c1"># key is a tuple of two numpy.int64s, which is the values</span>
 <span class="gp">... </span>    <span class="c1"># of &#39;id&#39; and &#39;ceil(df.v / 2)&#39; for the current group</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">key</span> <span class="o">+</span> <span class="p">(</span><span class="n">pdf</span><span class="o">.</span><span class="n">v</span><span class="o">.</span><span class="n">sum</span><span class="p">(),)])</span>
 <span class="gp">...</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">id</span><span class="p">,</span> <span class="n">ceil</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">v</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">applyInPandas</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">sum_func</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="s2">&quot;id long, `ceil(v / 2)` long, v double&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 <span class="go">+---+-----------+----+</span>
 <span class="go">| id|ceil(v / 2)|   v|</span>
 <span class="go">+---+-----------+----+</span>
 <span class="go">|  2|          5|10.0|</span>
 <span class="go">|  1|          1| 3.0|</span>
 <span class="go">|  2|          3| 5.0|</span>
 <span class="go">|  2|          2| 3.0|</span>
 <span class="go">+---+-----------+----+</span>
 </pre></div>
 </div>
 </dd></dl>

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