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| <div class="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 id="pyspark.sql.GroupedData.applyInPandas"> |
| <code class="sig-prename descclassname">GroupedData.</code><code class="sig-name descname">applyInPandas</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">func</span><span class="p">:</span> <span class="n">PandasGroupedMapFunction</span></em>, <em class="sig-param"><span class="n">schema</span><span class="p">:</span> <span class="n">Union<span class="p">[</span><a class="reference internal" href="pyspark.sql.types.StructType.html#pyspark.sql.types.StructType" title="pyspark.sql.types.StructType">pyspark.sql.types.StructType</a><span class="p">, </span>str<span class="p">]</span></span></em><span class="sig-paren">)</span> → pyspark.sql.dataframe.DataFrame<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>This API is experimental.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">pandas_udf</span><span class="p">,</span> <span class="n">ceil</span> |
| <span class="gp">>>> </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">"id"</span><span class="p">,</span> <span class="s2">"v"</span><span class="p">))</span> |
| <span class="gp">>>> </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">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"id"</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">"id long, v double"</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">>>> </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">"id"</span><span class="p">,</span> <span class="s2">"v"</span><span class="p">))</span> |
| <span class="gp">>>> </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 'id' 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">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'id'</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">"id long, v double"</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">>>> </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 'id' and 'ceil(df.v / 2)' 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">>>> </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">"id long, `ceil(v / 2)` long, v double"</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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