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   <h1>Source code for apache_beam.dataframe.partitionings</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="kn">import</span> <span class="nn">random</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">TypeVar</span>

 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>

 <span class="n">Frame</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s1">&#39;Frame&#39;</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">generic</span><span class="o">.</span><span class="n">NDFrame</span><span class="p">)</span>


 <div class="viewcode-block" id="Partitioning"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Partitioning">[docs]</a><span class="k">class</span> <span class="nc">Partitioning</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;A class representing a (consistent) partitioning of dataframe objects.</span>
 <span class="sd">  &quot;&quot;&quot;</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>

 <div class="viewcode-block" id="Partitioning.is_subpartitioning_of"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Partitioning.is_subpartitioning_of">[docs]</a>  <span class="k">def</span> <span class="nf">is_subpartitioning_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="c1"># type: (Partitioning) -&gt; bool</span>

 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns whether self is a sub-partition of other.</span>

 <span class="sd">    Specifically, returns whether something partitioned by self is necissarily</span>
 <span class="sd">    also partitioned by other.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

   <span class="k">def</span> <span class="fm">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">return</span> <span class="bp">self</span> <span class="o">!=</span> <span class="n">other</span> <span class="ow">and</span> <span class="bp">self</span> <span class="o">&lt;=</span> <span class="n">other</span>

   <span class="k">def</span> <span class="fm">__le__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">return</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_subpartitioning_of</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>

 <div class="viewcode-block" id="Partitioning.partition_fn"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Partitioning.partition_fn">[docs]</a>  <span class="k">def</span> <span class="nf">partition_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">num_partitions</span><span class="p">):</span>
     <span class="c1"># type: (Frame, int) -&gt; Iterable[Tuple[Any, Frame]]</span>

 <span class="w">    </span><span class="sd">&quot;&quot;&quot;A callable that actually performs the partitioning of a Frame df.</span>

 <span class="sd">    This will be invoked via a FlatMap in conjunction with a GroupKey to</span>
 <span class="sd">    achieve the desired partitioning.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

 <div class="viewcode-block" id="Partitioning.test_partition_fn"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Partitioning.test_partition_fn">[docs]</a>  <span class="k">def</span> <span class="nf">test_partition_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">):</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_fn</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span></div></div>


 <div class="viewcode-block" id="Index"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Index">[docs]</a><span class="k">class</span> <span class="nc">Index</span><span class="p">(</span><span class="n">Partitioning</span><span class="p">):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;A partitioning by index (either fully or partially).</span>

 <span class="sd">  If the set of &quot;levels&quot; of the index to consider is not specified, the entire</span>
 <span class="sd">  index is used.</span>

 <span class="sd">  These form a partial order, given by</span>

 <span class="sd">      Singleton() &lt; Index([i]) &lt; Index([i, j]) &lt; ... &lt; Index() &lt; Arbitrary()</span>

 <span class="sd">  The ordering is implemented via the is_subpartitioning_of method, where the</span>
 <span class="sd">  examples on the right are subpartitionings of the examples on the left above.</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">levels</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span> <span class="o">=</span> <span class="n">levels</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span><span class="p">:</span>
       <span class="k">return</span> <span class="s1">&#39;Index</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="k">return</span> <span class="s1">&#39;Index&#39;</span>

   <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">_levels</span>

   <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span><span class="p">:</span>
       <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_levels</span><span class="p">)))</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>

 <div class="viewcode-block" id="Index.is_subpartitioning_of"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Index.is_subpartitioning_of">[docs]</a>  <span class="k">def</span> <span class="nf">is_subpartitioning_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Singleton</span><span class="p">):</span>
       <span class="k">return</span> <span class="kc">True</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Index</span><span class="p">):</span>
       <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">return</span> <span class="kc">True</span>
       <span class="k">elif</span> <span class="n">other</span><span class="o">.</span><span class="n">_levels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">return</span> <span class="kc">False</span>
       <span class="k">else</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="n">level</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span> <span class="k">for</span> <span class="n">level</span> <span class="ow">in</span> <span class="n">other</span><span class="o">.</span><span class="n">_levels</span><span class="p">)</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="p">(</span><span class="n">Arbitrary</span><span class="p">,</span> <span class="n">JoinIndex</span><span class="p">)):</span>
       <span class="k">return</span> <span class="kc">False</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Encountered unknown type </span><span class="si">{</span><span class="n">other</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">)</span></div>

   <span class="k">def</span> <span class="nf">_hash_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">):</span>
     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
       <span class="n">levels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">nlevels</span><span class="p">))</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="n">levels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_levels</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span>
         <span class="n">pd</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">hash_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="n">level</span><span class="p">)))</span>
         <span class="k">for</span> <span class="n">level</span> <span class="ow">in</span> <span class="n">levels</span><span class="p">)</span>

 <div class="viewcode-block" id="Index.partition_fn"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Index.partition_fn">[docs]</a>  <span class="k">def</span> <span class="nf">partition_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">num_partitions</span><span class="p">):</span>
     <span class="n">hashes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hash_index</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
     <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_partitions</span><span class="p">):</span>
       <span class="k">yield</span> <span class="n">key</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">hashes</span> <span class="o">%</span> <span class="n">num_partitions</span> <span class="o">==</span> <span class="n">key</span><span class="p">]</span></div>

 <div class="viewcode-block" id="Index.check"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Index.check">[docs]</a>  <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dfs</span><span class="p">):</span>
     <span class="c1"># Drop empty DataFrames</span>
     <span class="n">dfs</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span> <span class="k">for</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">dfs</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)]</span>

     <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">dfs</span><span class="p">):</span>
       <span class="k">return</span> <span class="kc">True</span>

     <span class="k">def</span> <span class="nf">apply_consistent_order</span><span class="p">(</span><span class="n">dfs</span><span class="p">):</span>
       <span class="c1"># Apply consistent order between dataframes by using sum of the index&#39;s</span>
       <span class="c1"># hash.</span>
       <span class="c1"># Apply consistent order within dataframe with sort_index()</span>
       <span class="c1"># Also drops any empty dataframes.</span>
       <span class="k">return</span> <span class="nb">sorted</span><span class="p">((</span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span> <span class="k">for</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">dfs</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)),</span>
                     <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_hash_index</span><span class="p">(</span><span class="n">df</span><span class="p">)))</span>

     <span class="n">dfs</span> <span class="o">=</span> <span class="n">apply_consistent_order</span><span class="p">(</span><span class="n">dfs</span><span class="p">)</span>
     <span class="n">repartitioned_dfs</span> <span class="o">=</span> <span class="n">apply_consistent_order</span><span class="p">(</span>
         <span class="n">df</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_partition_fn</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">dfs</span><span class="p">)))</span>

     <span class="c1"># Assert that each index is identical</span>
     <span class="k">for</span> <span class="n">df</span><span class="p">,</span> <span class="n">repartitioned_df</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">dfs</span><span class="p">,</span> <span class="n">repartitioned_dfs</span><span class="p">):</span>
       <span class="k">if</span> <span class="ow">not</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">repartitioned_df</span><span class="o">.</span><span class="n">index</span><span class="p">):</span>
         <span class="k">return</span> <span class="kc">False</span>

     <span class="k">return</span> <span class="kc">True</span></div></div>


 <div class="viewcode-block" id="Singleton"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Singleton">[docs]</a><span class="k">class</span> <span class="nc">Singleton</span><span class="p">(</span><span class="n">Partitioning</span><span class="p">):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;A partitioning of all the data into a single partition.</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">reason</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_reason</span> <span class="o">=</span> <span class="n">reason</span>

   <span class="nd">@property</span>
   <span class="k">def</span> <span class="nf">reason</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reason</span>

   <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>

   <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>

 <div class="viewcode-block" id="Singleton.is_subpartitioning_of"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Singleton.is_subpartitioning_of">[docs]</a>  <span class="k">def</span> <span class="nf">is_subpartitioning_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Singleton</span><span class="p">)</span></div>

 <div class="viewcode-block" id="Singleton.partition_fn"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Singleton.partition_fn">[docs]</a>  <span class="k">def</span> <span class="nf">partition_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">num_partitions</span><span class="p">):</span>
     <span class="k">yield</span> <span class="kc">None</span><span class="p">,</span> <span class="n">df</span></div>

 <div class="viewcode-block" id="Singleton.check"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Singleton.check">[docs]</a>  <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dfs</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">dfs</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span></div></div>


 <div class="viewcode-block" id="JoinIndex"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.JoinIndex">[docs]</a><span class="k">class</span> <span class="nc">JoinIndex</span><span class="p">(</span><span class="n">Partitioning</span><span class="p">):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;A partitioning that lets two frames be joined.</span>
 <span class="sd">  This can either be a hash partitioning on the full index, or a common</span>
 <span class="sd">  ancestor with no intervening re-indexing/re-partitioning.</span>

 <span class="sd">  It fits into the partial ordering as</span>

 <span class="sd">      Index() &lt; JoinIndex(x) &lt; JoinIndex() &lt; Arbitrary()</span>

 <span class="sd">  with</span>

 <span class="sd">      JoinIndex(x) and JoinIndex(y)</span>

 <span class="sd">  being incomparable for nontrivial x != y.</span>

 <span class="sd">  Expressions desiring to make use of this index should simply declare a</span>
 <span class="sd">  requirement of JoinIndex().</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">ancestor</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_ancestor</span> <span class="o">=</span> <span class="n">ancestor</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ancestor</span><span class="p">:</span>
       <span class="k">return</span> <span class="s1">&#39;JoinIndex[</span><span class="si">%s</span><span class="s1">]&#39;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ancestor</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="k">return</span> <span class="s1">&#39;JoinIndex&#39;</span>

   <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">):</span>
       <span class="k">return</span> <span class="kc">False</span>
     <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ancestor</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
       <span class="k">return</span> <span class="n">other</span><span class="o">.</span><span class="n">_ancestor</span> <span class="ow">is</span> <span class="kc">None</span>
     <span class="k">elif</span> <span class="n">other</span><span class="o">.</span><span class="n">_ancestor</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
       <span class="k">return</span> <span class="kc">False</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ancestor</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">_ancestor</span>

   <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">hash</span><span class="p">((</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ancestor</span><span class="p">))</span>

 <div class="viewcode-block" id="JoinIndex.is_subpartitioning_of"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.JoinIndex.is_subpartitioning_of">[docs]</a>  <span class="k">def</span> <span class="nf">is_subpartitioning_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Arbitrary</span><span class="p">):</span>
       <span class="k">return</span> <span class="kc">False</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">JoinIndex</span><span class="p">):</span>
       <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ancestor</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span> <span class="o">==</span> <span class="n">other</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="k">return</span> <span class="kc">True</span></div>

 <div class="viewcode-block" id="JoinIndex.test_partition_fn"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.JoinIndex.test_partition_fn">[docs]</a>  <span class="k">def</span> <span class="nf">test_partition_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">Index</span><span class="p">()</span><span class="o">.</span><span class="n">test_partition_fn</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></div>

 <div class="viewcode-block" id="JoinIndex.check"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.JoinIndex.check">[docs]</a>  <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dfs</span><span class="p">):</span>
     <span class="k">return</span> <span class="kc">True</span></div></div>


 <div class="viewcode-block" id="Arbitrary"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Arbitrary">[docs]</a><span class="k">class</span> <span class="nc">Arbitrary</span><span class="p">(</span><span class="n">Partitioning</span><span class="p">):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;A partitioning imposing no constraints on the actual partitioning.</span>
 <span class="sd">  &quot;&quot;&quot;</span>
   <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>

   <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>

 <div class="viewcode-block" id="Arbitrary.is_subpartitioning_of"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Arbitrary.is_subpartitioning_of">[docs]</a>  <span class="k">def</span> <span class="nf">is_subpartitioning_of</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
     <span class="k">return</span> <span class="kc">True</span></div>

 <div class="viewcode-block" id="Arbitrary.test_partition_fn"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Arbitrary.test_partition_fn">[docs]</a>  <span class="k">def</span> <span class="nf">test_partition_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">):</span>
     <span class="n">num_partitions</span> <span class="o">=</span> <span class="mi">10</span>

     <span class="k">def</span> <span class="nf">shuffled</span><span class="p">(</span><span class="n">seq</span><span class="p">):</span>
       <span class="n">seq</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">seq</span><span class="p">)</span>
       <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">seq</span><span class="p">)</span>
       <span class="k">return</span> <span class="n">seq</span>

     <span class="n">part</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">shuffled</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))),</span> <span class="n">index</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> <span class="o">%</span> <span class="n">num_partitions</span>
     <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_partitions</span><span class="p">):</span>
       <span class="k">yield</span> <span class="n">k</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">part</span> <span class="o">==</span> <span class="n">k</span><span class="p">]</span></div>

 <div class="viewcode-block" id="Arbitrary.check"><a class="viewcode-back" href="../../../apache_beam.dataframe.partitionings.html#apache_beam.dataframe.partitionings.Arbitrary.check">[docs]</a>  <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dfs</span><span class="p">):</span>
     <span class="k">return</span> <span class="kc">True</span></div></div>
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