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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 "License"); 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 "AS IS" 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">pyspark.sql.column</span> <span class="kn">import</span> <span class="n">Column</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="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</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._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">"GroupedData"</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">...</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">])</span> <span class="o">-></span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">DataFrame</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">"GroupedData"</span><span class="p">)</span> <span class="o">-></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">...</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">])</span> <span class="o">-></span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">DataFrame</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">"GroupedData"</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">-></span> <span class="n">DataFrame</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.classic.column</span> <span class="kn">import</span> <span class="n">_to_seq</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="w"> </span><span class="sd">"""</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:: 1.3.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</span> |
| <span class="sd"> """</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="s2">"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="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="n">index</span> <span class="o">=</span> <span class="mi">26</span> <span class="c1"># index to truncate string from the JVM side</span> |
| <span class="n">jvm_string</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">toString</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">jvm_string</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">jvm_string</span><span class="p">)</span> <span class="o">></span> <span class="n">index</span> <span class="ow">and</span> <span class="n">jvm_string</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"["</span><span class="p">:</span> |
| <span class="k">return</span> <span class="sa">f</span><span class="s2">"GroupedData</span><span class="si">{</span><span class="n">jvm_string</span><span class="p">[</span><span class="n">index</span><span class="p">:]</span><span class="si">}</span><span class="s2">"</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">()</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">-></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">-></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">-></span> <span class="n">DataFrame</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""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"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</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"> >>> from pyspark.sql import functions as sf</span> |
| <span class="sd"> >>> from pyspark.sql.functions import pandas_udf, PandasUDFType</span> |
| <span class="sd"> >>> df = spark.createDataFrame(</span> |
| <span class="sd"> ... [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"])</span> |
| <span class="sd"> >>> df.show()</span> |
| <span class="sd"> +---+-----+</span> |
| <span class="sd"> |age| name|</span> |
| <span class="sd"> +---+-----+</span> |
| <span class="sd"> | 2|Alice|</span> |
| <span class="sd"> | 3|Alice|</span> |
| <span class="sd"> | 5| Bob|</span> |
| <span class="sd"> | 10| Bob|</span> |
| <span class="sd"> +---+-----+</span> |
| |
| <span class="sd"> Group-by name, and count each group.</span> |
| |
| <span class="sd"> >>> df.groupBy(df.name)</span> |
| <span class="sd"> GroupedData[grouping...: [name...], value: [age: bigint, name: string], type: GroupBy]</span> |
| |
| <span class="sd"> >>> df.groupBy(df.name).agg({"*": "count"}).sort("name").show()</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> | name|count(1)|</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> |Alice| 2|</span> |
| <span class="sd"> | Bob| 2|</span> |
| <span class="sd"> +-----+--------+</span> |
| |
| <span class="sd"> Group-by name, and calculate the minimum age.</span> |
| |
| <span class="sd"> >>> df.groupBy(df.name).agg(sf.min(df.age)).sort("name").show()</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> | name|min(age)|</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> |Alice| 2|</span> |
| <span class="sd"> | Bob| 5|</span> |
| <span class="sd"> +-----+--------+</span> |
| |
| <span class="sd"> Same as above but uses pandas UDF.</span> |
| |
| <span class="sd"> >>> @pandas_udf('int', PandasUDFType.GROUPED_AGG) # doctest: +SKIP</span> |
| <span class="sd"> ... def min_udf(v):</span> |
| <span class="sd"> ... return v.min()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> df.groupBy(df.name).agg(min_udf(df.age)).sort("name").show() # doctest: +SKIP</span> |
| <span class="sd"> +-----+------------+</span> |
| <span class="sd"> | name|min_udf(age)|</span> |
| <span class="sd"> +-----+------------+</span> |
| <span class="sd"> |Alice| 2|</span> |
| <span class="sd"> | Bob| 5|</span> |
| <span class="sd"> +-----+------------+</span> |
| <span class="sd"> """</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql.classic.column</span> <span class="kn">import</span> <span class="n">_to_seq</span> |
| |
| <span class="k">assert</span> <span class="n">exprs</span><span class="p">,</span> <span class="s2">"exprs should not be empty"</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">"all exprs should be Column"</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">-></span> <span class="n">DataFrame</span><span class="p">:</span> <span class="c1"># type: ignore[empty-body]</span> |
| <span class="w"> </span><span class="sd">"""Counts the number of records for each group.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> df = spark.createDataFrame(</span> |
| <span class="sd"> ... [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"])</span> |
| <span class="sd"> >>> df.show()</span> |
| <span class="sd"> +---+-----+</span> |
| <span class="sd"> |age| name|</span> |
| <span class="sd"> +---+-----+</span> |
| <span class="sd"> | 2|Alice|</span> |
| <span class="sd"> | 3|Alice|</span> |
| <span class="sd"> | 5| Bob|</span> |
| <span class="sd"> | 10| Bob|</span> |
| <span class="sd"> +---+-----+</span> |
| |
| <span class="sd"> Group-by name, and count each group.</span> |
| |
| <span class="sd"> >>> df.groupBy(df.name).count().sort("name").show()</span> |
| <span class="sd"> +-----+-----+</span> |
| <span class="sd"> | name|count|</span> |
| <span class="sd"> +-----+-----+</span> |
| <span class="sd"> |Alice| 2|</span> |
| <span class="sd"> | Bob| 2|</span> |
| <span class="sd"> +-----+-----+</span> |
| <span class="sd"> """</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">-></span> <span class="n">DataFrame</span><span class="p">:</span> <span class="c1"># type: ignore[empty-body]</span> |
| <span class="w"> </span><span class="sd">"""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"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</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"> """</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">-></span> <span class="n">DataFrame</span><span class="p">:</span> <span class="c1"># type: ignore[empty-body]</span> |
| <span class="w"> </span><span class="sd">"""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"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</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"> >>> df = spark.createDataFrame([</span> |
| <span class="sd"> ... (2, "Alice", 80), (3, "Alice", 100),</span> |
| <span class="sd"> ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])</span> |
| <span class="sd"> >>> df.show()</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> |age| name|height|</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> | 2|Alice| 80|</span> |
| <span class="sd"> | 3|Alice| 100|</span> |
| <span class="sd"> | 5| Bob| 120|</span> |
| <span class="sd"> | 10| Bob| 140|</span> |
| <span class="sd"> +---+-----+------+</span> |
| |
| <span class="sd"> Group-by name, and calculate the mean of the age in each group.</span> |
| |
| <span class="sd"> >>> df.groupBy("name").avg('age').sort("name").show()</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> | name|avg(age)|</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> |Alice| 2.5|</span> |
| <span class="sd"> | Bob| 7.5|</span> |
| <span class="sd"> +-----+--------+</span> |
| |
| <span class="sd"> Calculate the mean of the age and height in all data.</span> |
| |
| <span class="sd"> >>> df.groupBy().avg('age', 'height').show()</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> |avg(age)|avg(height)|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> | 5.0| 110.0|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> """</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">-></span> <span class="n">DataFrame</span><span class="p">:</span> <span class="c1"># type: ignore[empty-body]</span> |
| <span class="w"> </span><span class="sd">"""Computes the max value for each numeric columns for each group.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> df = spark.createDataFrame([</span> |
| <span class="sd"> ... (2, "Alice", 80), (3, "Alice", 100),</span> |
| <span class="sd"> ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])</span> |
| <span class="sd"> >>> df.show()</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> |age| name|height|</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> | 2|Alice| 80|</span> |
| <span class="sd"> | 3|Alice| 100|</span> |
| <span class="sd"> | 5| Bob| 120|</span> |
| <span class="sd"> | 10| Bob| 140|</span> |
| <span class="sd"> +---+-----+------+</span> |
| |
| <span class="sd"> Group-by name, and calculate the max of the age in each group.</span> |
| |
| <span class="sd"> >>> df.groupBy("name").max("age").sort("name").show()</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> | name|max(age)|</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> |Alice| 3|</span> |
| <span class="sd"> | Bob| 10|</span> |
| <span class="sd"> +-----+--------+</span> |
| |
| <span class="sd"> Calculate the max of the age and height in all data.</span> |
| |
| <span class="sd"> >>> df.groupBy().max("age", "height").show()</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> |max(age)|max(height)|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> | 10| 140|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> """</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">-></span> <span class="n">DataFrame</span><span class="p">:</span> <span class="c1"># type: ignore[empty-body]</span> |
| <span class="w"> </span><span class="sd">"""Computes the min value for each numeric column for each group.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</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"> >>> df = spark.createDataFrame([</span> |
| <span class="sd"> ... (2, "Alice", 80), (3, "Alice", 100),</span> |
| <span class="sd"> ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])</span> |
| <span class="sd"> >>> df.show()</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> |age| name|height|</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> | 2|Alice| 80|</span> |
| <span class="sd"> | 3|Alice| 100|</span> |
| <span class="sd"> | 5| Bob| 120|</span> |
| <span class="sd"> | 10| Bob| 140|</span> |
| <span class="sd"> +---+-----+------+</span> |
| |
| <span class="sd"> Group-by name, and calculate the min of the age in each group.</span> |
| |
| <span class="sd"> >>> df.groupBy("name").min("age").sort("name").show()</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> | name|min(age)|</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> |Alice| 2|</span> |
| <span class="sd"> | Bob| 5|</span> |
| <span class="sd"> +-----+--------+</span> |
| |
| <span class="sd"> Calculate the min of the age and height in all data.</span> |
| |
| <span class="sd"> >>> df.groupBy().min("age", "height").show()</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> |min(age)|min(height)|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> | 2| 80|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> """</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">-></span> <span class="n">DataFrame</span><span class="p">:</span> <span class="c1"># type: ignore[empty-body]</span> |
| <span class="w"> </span><span class="sd">"""Computes the sum for each numeric columns for each group.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</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"> >>> df = spark.createDataFrame([</span> |
| <span class="sd"> ... (2, "Alice", 80), (3, "Alice", 100),</span> |
| <span class="sd"> ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"])</span> |
| <span class="sd"> >>> df.show()</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> |age| name|height|</span> |
| <span class="sd"> +---+-----+------+</span> |
| <span class="sd"> | 2|Alice| 80|</span> |
| <span class="sd"> | 3|Alice| 100|</span> |
| <span class="sd"> | 5| Bob| 120|</span> |
| <span class="sd"> | 10| Bob| 140|</span> |
| <span class="sd"> +---+-----+------+</span> |
| |
| <span class="sd"> Group-by name, and calculate the sum of the age in each group.</span> |
| |
| <span class="sd"> >>> df.groupBy("name").sum("age").sort("name").show()</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> | name|sum(age)|</span> |
| <span class="sd"> +-----+--------+</span> |
| <span class="sd"> |Alice| 5|</span> |
| <span class="sd"> | Bob| 15|</span> |
| <span class="sd"> +-----+--------+</span> |
| |
| <span class="sd"> Calculate the sum of the age and height in all data.</span> |
| |
| <span class="sd"> >>> df.groupBy().sum("age", "height").show()</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> |sum(age)|sum(height)|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> | 20| 440|</span> |
| <span class="sd"> +--------+-----------+</span> |
| <span class="sd"> """</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">"LiteralType"</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="s2">"GroupedData"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Pivots a column of the current :class:`DataFrame` and performs the specified aggregation.</span> |
| |
| <span class="sd"> .. versionadded:: 1.6.0</span> |
| |
| <span class="sd"> .. versionchanged:: 3.4.0</span> |
| <span class="sd"> Supports Spark Connect.</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"> If ``values`` is not provided, Spark will eagerly compute the distinct values in</span> |
| <span class="sd"> ``pivot_col`` so it can determine the resulting schema of the transformation. To avoid</span> |
| <span class="sd"> any eager computations, provide an explicit list of values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> from pyspark.sql import Row</span> |
| <span class="sd"> >>> df1 = spark.createDataFrame([</span> |
| <span class="sd"> ... Row(course="dotNET", year=2012, earnings=10000),</span> |
| <span class="sd"> ... Row(course="Java", year=2012, earnings=20000),</span> |
| <span class="sd"> ... Row(course="dotNET", year=2012, earnings=5000),</span> |
| <span class="sd"> ... Row(course="dotNET", year=2013, earnings=48000),</span> |
| <span class="sd"> ... Row(course="Java", year=2013, earnings=30000),</span> |
| <span class="sd"> ... ])</span> |
| <span class="sd"> >>> df1.show()</span> |
| <span class="sd"> +------+----+--------+</span> |
| <span class="sd"> |course|year|earnings|</span> |
| <span class="sd"> +------+----+--------+</span> |
| <span class="sd"> |dotNET|2012| 10000|</span> |
| <span class="sd"> | Java|2012| 20000|</span> |
| <span class="sd"> |dotNET|2012| 5000|</span> |
| <span class="sd"> |dotNET|2013| 48000|</span> |
| <span class="sd"> | Java|2013| 30000|</span> |
| <span class="sd"> +------+----+--------+</span> |
| <span class="sd"> >>> df2 = spark.createDataFrame([</span> |
| <span class="sd"> ... Row(training="expert", sales=Row(course="dotNET", year=2012, earnings=10000)),</span> |
| <span class="sd"> ... Row(training="junior", sales=Row(course="Java", year=2012, earnings=20000)),</span> |
| <span class="sd"> ... Row(training="expert", sales=Row(course="dotNET", year=2012, earnings=5000)),</span> |
| <span class="sd"> ... Row(training="junior", sales=Row(course="dotNET", year=2013, earnings=48000)),</span> |
| <span class="sd"> ... Row(training="expert", sales=Row(course="Java", year=2013, earnings=30000)),</span> |
| <span class="sd"> ... ]) # doctest: +SKIP</span> |
| <span class="sd"> >>> df2.show() # doctest: +SKIP</span> |
| <span class="sd"> +--------+--------------------+</span> |
| <span class="sd"> |training| sales|</span> |
| <span class="sd"> +--------+--------------------+</span> |
| <span class="sd"> | expert|{dotNET, 2012, 10...|</span> |
| <span class="sd"> | junior| {Java, 2012, 20000}|</span> |
| <span class="sd"> | expert|{dotNET, 2012, 5000}|</span> |
| <span class="sd"> | junior|{dotNET, 2013, 48...|</span> |
| <span class="sd"> | expert| {Java, 2013, 30000}|</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"> >>> df1.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").show()</span> |
| <span class="sd"> +----+------+-----+</span> |
| <span class="sd"> |year|dotNET| Java|</span> |
| <span class="sd"> +----+------+-----+</span> |
| <span class="sd"> |2012| 15000|20000|</span> |
| <span class="sd"> |2013| 48000|30000|</span> |
| <span class="sd"> +----+------+-----+</span> |
| |
| <span class="sd"> Or without specifying column values (less efficient)</span> |
| |
| <span class="sd"> >>> df1.groupBy("year").pivot("course").sum("earnings").show()</span> |
| <span class="sd"> +----+-----+------+</span> |
| <span class="sd"> |year| Java|dotNET|</span> |
| <span class="sd"> +----+-----+------+</span> |
| <span class="sd"> |2012|20000| 15000|</span> |
| <span class="sd"> |2013|30000| 48000|</span> |
| <span class="sd"> +----+-----+------+</span> |
| <span class="sd"> >>> df2.groupBy("sales.year").pivot("sales.course").sum("sales.earnings").show()</span> |
| <span class="sd"> ... # doctest: +SKIP</span> |
| <span class="sd"> +----+-----+------+</span> |
| <span class="sd"> |year| Java|dotNET|</span> |
| <span class="sd"> +----+-----+------+</span> |
| <span class="sd"> |2012|20000| 15000|</span> |
| <span class="sd"> |2013|30000| 48000|</span> |
| <span class="sd"> +----+-----+------+</span> |
| <span class="sd"> """</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">-></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">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">"local[4]"</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">"sql.group tests"</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span> |
| <span class="n">globs</span><span class="p">[</span><span class="s2">"spark"</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</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">"__main__"</span><span class="p">:</span> |
| <span class="n">_test</span><span class="p">()</span> |
| </pre></div> |
| |
| </article> |
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