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<h1>Source code for pyspark.pandas.resample</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">A wrapper for ResampledData to behave like pandas Resampler.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">distutils.version</span><span class="w"> </span><span class="kn">import</span> <span class="n">LooseVersion</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">partial</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">Any</span><span class="p">,</span>
<span class="n">Generic</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">Union</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pandas.tseries.frequencies</span><span class="w"> </span><span class="kn">import</span> <span class="n">to_offset</span>
<span class="k">if</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="s2">&quot;1.3.0&quot;</span><span class="p">):</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pandas.core.common</span><span class="w"> </span><span class="kn">import</span> <span class="n">_builtin_table</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="k">else</span><span class="p">:</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pandas.core.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">SelectionMixin</span>
<span class="n">_builtin_table</span> <span class="o">=</span> <span class="n">SelectionMixin</span><span class="o">.</span><span class="n">_builtin_table</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark</span><span class="w"> </span><span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">Column</span><span class="p">,</span> <span class="n">functions</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.types</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">NumericType</span><span class="p">,</span>
<span class="n">StructField</span><span class="p">,</span>
<span class="n">TimestampNTZType</span><span class="p">,</span>
<span class="n">DataType</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark</span><span class="w"> </span><span class="kn">import</span> <span class="n">pandas</span> <span class="k">as</span> <span class="n">ps</span> <span class="c1"># For running doctests and reference resolution in PyCharm.</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas._typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">FrameLike</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas.frame</span><span class="w"> </span><span class="kn">import</span> <span class="n">DataFrame</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas.internal</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">InternalField</span><span class="p">,</span>
<span class="n">InternalFrame</span><span class="p">,</span>
<span class="n">SPARK_DEFAULT_INDEX_NAME</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas.missing.resample</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">MissingPandasLikeDataFrameResampler</span><span class="p">,</span>
<span class="n">MissingPandasLikeSeriesResampler</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas.series</span><span class="w"> </span><span class="kn">import</span> <span class="n">Series</span><span class="p">,</span> <span class="n">first_series</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas.utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">scol_for</span><span class="p">,</span>
<span class="n">verify_temp_column_name</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">is_remote</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas.spark.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">timestampdiff</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Resampler</span><span class="p">(</span><span class="n">Generic</span><span class="p">[</span><span class="n">FrameLike</span><span class="p">],</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Class for resampling datetimelike data, a groupby-like operation.</span>
<span class="sd"> It&#39;s easiest to use obj.resample(...) to use Resampler.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> psdf : DataFrame</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> a Resampler of the appropriate type</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> After resampling, see aggregate, apply, and transform functions.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">psdf</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span>
<span class="n">resamplekey</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Series</span><span class="p">],</span>
<span class="n">rule</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">closed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">label</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">agg_columns</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Series</span><span class="p">]</span> <span class="o">=</span> <span class="p">[],</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_psdf</span> <span class="o">=</span> <span class="n">psdf</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey</span> <span class="o">=</span> <span class="n">resamplekey</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_offset</span> <span class="o">=</span> <span class="n">to_offset</span><span class="p">(</span><span class="n">rule</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="o">.</span><span class="n">rule_code</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;A-DEC&quot;</span><span class="p">,</span> <span class="s2">&quot;M&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="s2">&quot;H&quot;</span><span class="p">,</span> <span class="s2">&quot;T&quot;</span><span class="p">,</span> <span class="s2">&quot;S&quot;</span><span class="p">]:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;rule code </span><span class="si">{}</span><span class="s2"> is not supported&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="o">.</span><span class="n">rule_code</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="p">,</span> <span class="s2">&quot;n&quot;</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;rule offset must be positive&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">closed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_closed</span> <span class="o">=</span> <span class="s2">&quot;right&quot;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="o">.</span><span class="n">rule_code</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;A-DEC&quot;</span><span class="p">,</span> <span class="s2">&quot;M&quot;</span><span class="p">]</span> <span class="k">else</span> <span class="s2">&quot;left&quot;</span>
<span class="k">elif</span> <span class="n">closed</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="s2">&quot;right&quot;</span><span class="p">]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_closed</span> <span class="o">=</span> <span class="n">closed</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="s2">&quot;invalid closed: &#39;</span><span class="si">{}</span><span class="s2">&#39;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">closed</span><span class="p">))</span>
<span class="k">if</span> <span class="n">label</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label</span> <span class="o">=</span> <span class="s2">&quot;right&quot;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="o">.</span><span class="n">rule_code</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;A-DEC&quot;</span><span class="p">,</span> <span class="s2">&quot;M&quot;</span><span class="p">]</span> <span class="k">else</span> <span class="s2">&quot;left&quot;</span>
<span class="k">elif</span> <span class="n">label</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="s2">&quot;right&quot;</span><span class="p">]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label</span> <span class="o">=</span> <span class="n">label</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="s2">&quot;invalid label: &#39;</span><span class="si">{}</span><span class="s2">&#39;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_agg_columns</span> <span class="o">=</span> <span class="n">agg_columns</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_resamplekey_scol</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Column</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_psdf</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">column</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">_resamplekey</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">column</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_resamplekey_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataType</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_psdf</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">data_type</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">_resamplekey</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">data_type</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_agg_columns_scols</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">Column</span><span class="p">]:</span>
<span class="k">return</span> <span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">column</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_agg_columns</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_make_interval</span><span class="p">(</span> <span class="c1"># type: ignore[return]</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">unit</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">col</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="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Column</span><span class="p">:</span>
<span class="k">if</span> <span class="n">is_remote</span><span class="p">():</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.connect.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">lit</span><span class="p">,</span> <span class="n">make_interval</span>
<span class="n">col</span> <span class="o">=</span> <span class="n">col</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span> <span class="k">else</span> <span class="n">lit</span><span class="p">(</span><span class="n">col</span><span class="p">)</span> <span class="c1"># type: ignore[assignment]</span>
<span class="k">if</span> <span class="n">unit</span> <span class="o">==</span> <span class="s2">&quot;MONTH&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">make_interval</span><span class="p">(</span><span class="n">months</span><span class="o">=</span><span class="n">col</span><span class="p">)</span> <span class="c1"># type: ignore</span>
<span class="k">if</span> <span class="n">unit</span> <span class="o">==</span> <span class="s2">&quot;HOUR&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">make_interval</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">col</span><span class="p">)</span> <span class="c1"># type: ignore</span>
<span class="k">if</span> <span class="n">unit</span> <span class="o">==</span> <span class="s2">&quot;MINUTE&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">make_interval</span><span class="p">(</span><span class="n">mins</span><span class="o">=</span><span class="n">col</span><span class="p">)</span> <span class="c1"># type: ignore</span>
<span class="k">if</span> <span class="n">unit</span> <span class="o">==</span> <span class="s2">&quot;SECOND&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">make_interval</span><span class="p">(</span><span class="n">secs</span><span class="o">=</span><span class="n">col</span><span class="p">)</span> <span class="c1"># type: ignore</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">sql_utils</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonSQLUtils</span>
<span class="n">col</span> <span class="o">=</span> <span class="n">col</span><span class="o">.</span><span class="n">_jc</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">Column</span><span class="p">)</span> <span class="k">else</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">_jc</span>
<span class="k">return</span> <span class="n">sql_utils</span><span class="o">.</span><span class="n">makeInterval</span><span class="p">(</span><span class="n">unit</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_bin_timestamp</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">,</span> <span class="n">ts_scol</span><span class="p">:</span> <span class="n">Column</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Column</span><span class="p">:</span>
<span class="n">key_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey_type</span>
<span class="n">origin_scol</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="n">origin</span><span class="p">)</span>
<span class="p">(</span><span class="n">rule_code</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="o">.</span><span class="n">rule_code</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="p">,</span> <span class="s2">&quot;n&quot;</span><span class="p">))</span>
<span class="n">left_closed</span><span class="p">,</span> <span class="n">right_closed</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_closed</span> <span class="o">==</span> <span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_closed</span> <span class="o">==</span> <span class="s2">&quot;right&quot;</span><span class="p">)</span>
<span class="n">left_labeled</span><span class="p">,</span> <span class="n">right_labeled</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_label</span> <span class="o">==</span> <span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label</span> <span class="o">==</span> <span class="s2">&quot;right&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">rule_code</span> <span class="o">==</span> <span class="s2">&quot;A-DEC&quot;</span><span class="p">:</span>
<span class="k">assert</span> <span class="p">(</span>
<span class="n">origin</span><span class="o">.</span><span class="n">month</span> <span class="o">==</span> <span class="mi">12</span>
<span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">day</span> <span class="o">==</span> <span class="mi">31</span>
<span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">hour</span> <span class="o">==</span> <span class="mi">0</span>
<span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">minute</span> <span class="o">==</span> <span class="mi">0</span>
<span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">second</span> <span class="o">==</span> <span class="mi">0</span>
<span class="p">)</span>
<span class="n">diff</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">-</span> <span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">origin_scol</span><span class="p">)</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">(</span><span class="n">diff</span> <span class="o">%</span> <span class="n">n</span><span class="p">)</span>
<span class="n">edge_cond</span> <span class="o">=</span> <span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">month</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">12</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">dayofmonth</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">31</span><span class="p">)</span>
<span class="n">edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span>
<span class="k">if</span> <span class="n">left_closed</span> <span class="ow">and</span> <span class="n">right_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">+=</span> <span class="n">n</span>
<span class="k">elif</span> <span class="n">right_closed</span> <span class="ow">and</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">-=</span> <span class="n">n</span>
<span class="k">if</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">-</span> <span class="n">n</span><span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">-</span> <span class="n">mod</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">))</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">mod</span> <span class="o">-</span> <span class="n">n</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">to_timestamp</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">make_date</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">edge_cond</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span><span class="n">non_edge_label</span><span class="p">),</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="mi">12</span><span class="p">),</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="mi">31</span><span class="p">)</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="n">rule_code</span> <span class="o">==</span> <span class="s2">&quot;M&quot;</span><span class="p">:</span>
<span class="k">assert</span> <span class="p">(</span>
<span class="n">origin</span><span class="o">.</span><span class="n">is_month_end</span>
<span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">hour</span> <span class="o">==</span> <span class="mi">0</span>
<span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">minute</span> <span class="o">==</span> <span class="mi">0</span>
<span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">second</span> <span class="o">==</span> <span class="mi">0</span>
<span class="p">)</span>
<span class="n">diff</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">-</span> <span class="n">F</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">origin_scol</span><span class="p">))</span> <span class="o">*</span> <span class="mi">12</span>
<span class="o">+</span> <span class="n">F</span><span class="o">.</span><span class="n">month</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span>
<span class="o">-</span> <span class="n">F</span><span class="o">.</span><span class="n">month</span><span class="p">(</span><span class="n">origin_scol</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">(</span><span class="n">diff</span> <span class="o">%</span> <span class="n">n</span><span class="p">)</span>
<span class="n">edge_cond</span> <span class="o">=</span> <span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">dayofmonth</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="n">F</span><span class="o">.</span><span class="n">dayofmonth</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">last_day</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)))</span>
<span class="n">truncated_ts_scol</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;MONTH&quot;</span><span class="p">,</span> <span class="n">ts_scol</span><span class="p">)</span>
<span class="n">edge_label</span> <span class="o">=</span> <span class="n">truncated_ts_scol</span>
<span class="k">if</span> <span class="n">left_closed</span> <span class="ow">and</span> <span class="n">right_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="s2">&quot;MONTH&quot;</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">right_closed</span> <span class="ow">and</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="s2">&quot;MONTH&quot;</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">if</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span>
<span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">truncated_ts_scol</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="s2">&quot;MONTH&quot;</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span>
<span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span><span class="n">truncated_ts_scol</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="s2">&quot;MONTH&quot;</span><span class="p">,</span> <span class="n">mod</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">truncated_ts_scol</span><span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span>
<span class="n">truncated_ts_scol</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="s2">&quot;MONTH&quot;</span><span class="p">,</span> <span class="n">mod</span> <span class="o">-</span> <span class="n">n</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">to_timestamp</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">last_day</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">edge_cond</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span><span class="n">non_edge_label</span><span class="p">))</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="n">rule_code</span> <span class="o">==</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">origin</span><span class="o">.</span><span class="n">hour</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">minute</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">second</span> <span class="o">==</span> <span class="mi">0</span>
<span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># NOTE: the logic to process &#39;1D&#39; is different from the cases with n&gt;1,</span>
<span class="c1"># since hour/minute/second parts are taken into account to determine edges!</span>
<span class="n">edge_cond</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">hour</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">minute</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">second</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">left_closed</span> <span class="ow">and</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;DAY&quot;</span><span class="p">,</span> <span class="n">ts_scol</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">left_closed</span> <span class="ow">and</span> <span class="n">right_labeled</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;DAY&quot;</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">date_add</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">right_closed</span> <span class="ow">and</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">edge_cond</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;DAY&quot;</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">date_sub</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;DAY&quot;</span><span class="p">,</span> <span class="n">ts_scol</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">edge_cond</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;DAY&quot;</span><span class="p">,</span> <span class="n">ts_scol</span><span class="p">))</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;DAY&quot;</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">date_add</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">diff</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">datediff</span><span class="p">(</span><span class="n">end</span><span class="o">=</span><span class="n">ts_scol</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">origin_scol</span><span class="p">)</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">diff</span> <span class="o">%</span> <span class="n">n</span>
<span class="n">edge_cond</span> <span class="o">=</span> <span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span>
<span class="n">truncated_ts_scol</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="s2">&quot;DAY&quot;</span><span class="p">,</span> <span class="n">ts_scol</span><span class="p">)</span>
<span class="n">edge_label</span> <span class="o">=</span> <span class="n">truncated_ts_scol</span>
<span class="k">if</span> <span class="n">left_closed</span> <span class="ow">and</span> <span class="n">right_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_add</span><span class="p">(</span><span class="n">truncated_ts_scol</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">right_closed</span> <span class="ow">and</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_sub</span><span class="p">(</span><span class="n">truncated_ts_scol</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">if</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_sub</span><span class="p">(</span><span class="n">truncated_ts_scol</span><span class="p">,</span> <span class="n">mod</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_sub</span><span class="p">(</span><span class="n">truncated_ts_scol</span><span class="p">,</span> <span class="n">mod</span> <span class="o">-</span> <span class="n">n</span><span class="p">)</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">edge_cond</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span><span class="n">non_edge_label</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">rule_code</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;H&quot;</span><span class="p">,</span> <span class="s2">&quot;T&quot;</span><span class="p">,</span> <span class="s2">&quot;S&quot;</span><span class="p">]:</span>
<span class="n">unit_mapping</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;H&quot;</span><span class="p">:</span> <span class="s2">&quot;HOUR&quot;</span><span class="p">,</span> <span class="s2">&quot;T&quot;</span><span class="p">:</span> <span class="s2">&quot;MINUTE&quot;</span><span class="p">,</span> <span class="s2">&quot;S&quot;</span><span class="p">:</span> <span class="s2">&quot;SECOND&quot;</span><span class="p">}</span>
<span class="n">unit_str</span> <span class="o">=</span> <span class="n">unit_mapping</span><span class="p">[</span><span class="n">rule_code</span><span class="p">]</span>
<span class="n">truncated_ts_scol</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">date_trunc</span><span class="p">(</span><span class="n">unit_str</span><span class="p">,</span> <span class="n">ts_scol</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key_type</span><span class="p">,</span> <span class="n">TimestampNTZType</span><span class="p">):</span>
<span class="n">truncated_ts_scol</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">to_timestamp_ntz</span><span class="p">(</span><span class="n">truncated_ts_scol</span><span class="p">)</span>
<span class="n">diff</span> <span class="o">=</span> <span class="n">timestampdiff</span><span class="p">(</span><span class="n">unit_str</span><span class="p">,</span> <span class="n">origin_scol</span><span class="p">,</span> <span class="n">truncated_ts_scol</span><span class="p">)</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">(</span><span class="n">diff</span> <span class="o">%</span> <span class="n">F</span><span class="o">.</span><span class="n">lit</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
<span class="k">if</span> <span class="n">rule_code</span> <span class="o">==</span> <span class="s2">&quot;H&quot;</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">origin</span><span class="o">.</span><span class="n">minute</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">origin</span><span class="o">.</span><span class="n">second</span> <span class="o">==</span> <span class="mi">0</span>
<span class="n">edge_cond</span> <span class="o">=</span> <span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">minute</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">second</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">rule_code</span> <span class="o">==</span> <span class="s2">&quot;T&quot;</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">origin</span><span class="o">.</span><span class="n">second</span> <span class="o">==</span> <span class="mi">0</span>
<span class="n">edge_cond</span> <span class="o">=</span> <span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">second</span><span class="p">(</span><span class="n">ts_scol</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">edge_cond</span> <span class="o">=</span> <span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span>
<span class="n">edge_label</span> <span class="o">=</span> <span class="n">truncated_ts_scol</span>
<span class="k">if</span> <span class="n">left_closed</span> <span class="ow">and</span> <span class="n">right_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="n">unit_str</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">right_closed</span> <span class="ow">and</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">edge_label</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="n">unit_str</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">if</span> <span class="n">left_labeled</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">truncated_ts_scol</span><span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span>
<span class="n">truncated_ts_scol</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="n">unit_str</span><span class="p">,</span> <span class="n">mod</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">non_edge_label</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span>
<span class="n">mod</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">truncated_ts_scol</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="n">unit_str</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span>
<span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span><span class="n">truncated_ts_scol</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_make_interval</span><span class="p">(</span><span class="n">unit_str</span><span class="p">,</span> <span class="n">mod</span> <span class="o">-</span> <span class="n">n</span><span class="p">))</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">edge_cond</span><span class="p">,</span> <span class="n">edge_label</span><span class="p">)</span><span class="o">.</span><span class="n">otherwise</span><span class="p">(</span><span class="n">non_edge_label</span><span class="p">)</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="s2">&quot;Got the unexpected unit </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rule_code</span><span class="p">))</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key_type</span><span class="p">,</span> <span class="n">TimestampNTZType</span><span class="p">):</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">to_timestamp_ntz</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">ret</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_downsample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Downsample the defined function.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> how : string / mapped function</span>
<span class="sd"> **kwargs : kw args passed to how function</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># a simple example to illustrate the computation:</span>
<span class="c1"># dates = [</span>
<span class="c1"># datetime(2012, 1, 2),</span>
<span class="c1"># datetime(2012, 5, 3),</span>
<span class="c1"># datetime(2022, 5, 3),</span>
<span class="c1"># ]</span>
<span class="c1"># index = pd.DatetimeIndex(dates)</span>
<span class="c1"># pdf = pd.DataFrame(np.array([1,2,3]), index=index, columns=[&#39;A&#39;])</span>
<span class="c1"># pdf.resample(&#39;3Y&#39;).max()</span>
<span class="c1"># A</span>
<span class="c1"># 2012-12-31 2.0</span>
<span class="c1"># 2015-12-31 NaN</span>
<span class="c1"># 2018-12-31 NaN</span>
<span class="c1"># 2021-12-31 NaN</span>
<span class="c1"># 2024-12-31 3.0</span>
<span class="c1">#</span>
<span class="c1"># in this case:</span>
<span class="c1"># 1, obtain one origin point to bin all timestamps, we can get one (2009-12-31)</span>
<span class="c1"># from the minimum timestamp (2012-01-02);</span>
<span class="c1"># 2, the default intervals for &#39;Y&#39; are right-closed, so intervals are:</span>
<span class="c1"># (2009-12-31, 2012-12-31], (2012-12-31, 2015-12-31], (2015-12-31, 2018-12-31], ...</span>
<span class="c1"># 3, bin all timestamps, for example, 2022-05-03 belongs to interval</span>
<span class="c1"># (2021-12-31, 2024-12-31], since the default label is &#39;right&#39;, label it with the right</span>
<span class="c1"># edge 2024-12-31;</span>
<span class="c1"># 4, some intervals maybe too large for this down sampling, so we need to pad the dataframe</span>
<span class="c1"># to avoid missing some results, like: 2015-12-31, 2018-12-31 and 2021-12-31;</span>
<span class="c1"># 5, union the binned dataframe and padded dataframe, and apply aggregation &#39;max&#39; to get</span>
<span class="c1"># the final results;</span>
<span class="c1"># one action to obtain the range, in the future we may cache it in the index.</span>
<span class="n">ts_min</span><span class="p">,</span> <span class="n">ts_max</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_psdf</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">spark_frame</span><span class="o">.</span><span class="n">select</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey_scol</span><span class="p">),</span> <span class="n">F</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey_scol</span><span class="p">)</span>
<span class="p">)</span>
<span class="o">.</span><span class="n">toPandas</span><span class="p">()</span>
<span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># the logic to obtain an origin point to bin the timestamps is too complex to follow,</span>
<span class="c1"># here just use Pandas&#39; resample on a 1-length series to get it.</span>
<span class="n">ts_origin</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="n">ts_min</span><span class="p">])</span>
<span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="n">rule</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="o">.</span><span class="n">freqstr</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_closed</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">assert</span> <span class="n">ts_origin</span> <span class="o">&lt;=</span> <span class="n">ts_min</span>
<span class="n">bin_col_name</span> <span class="o">=</span> <span class="s2">&quot;__tmp_resample_bin_col__&quot;</span>
<span class="n">bin_col_label</span> <span class="o">=</span> <span class="n">verify_temp_column_name</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_psdf</span><span class="p">,</span> <span class="n">bin_col_name</span><span class="p">)</span>
<span class="n">bin_col_field</span> <span class="o">=</span> <span class="n">InternalField</span><span class="p">(</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="s2">&quot;datetime64[ns]&quot;</span><span class="p">),</span>
<span class="n">struct_field</span><span class="o">=</span><span class="n">StructField</span><span class="p">(</span><span class="n">bin_col_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey_type</span><span class="p">,</span> <span class="kc">True</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">bin_scol</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bin_timestamp</span><span class="p">(</span><span class="n">ts_origin</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resamplekey_scol</span><span class="p">)</span>
<span class="n">agg_columns</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">psser</span> <span class="k">for</span> <span class="n">psser</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_agg_columns</span> <span class="k">if</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">psser</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">data_type</span><span class="p">,</span> <span class="n">NumericType</span><span class="p">))</span>
<span class="p">]</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">agg_columns</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
<span class="c1"># in the binning side, label the timestamps according to the origin and the freq(rule)</span>
<span class="n">bin_sdf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_psdf</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">spark_frame</span><span class="o">.</span><span class="n">select</span><span class="p">(</span>
<span class="n">F</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="n">SPARK_DEFAULT_INDEX_NAME</span><span class="p">),</span>
<span class="n">bin_scol</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="n">bin_col_name</span><span class="p">),</span>
<span class="o">*</span><span class="p">[</span><span class="n">psser</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">column</span> <span class="k">for</span> <span class="n">psser</span> <span class="ow">in</span> <span class="n">agg_columns</span><span class="p">],</span>
<span class="p">)</span>
<span class="c1"># in the padding side, insert necessary points</span>
<span class="c1"># again, directly apply Pandas&#39; resample on a 2-length series to obtain the indices</span>
<span class="n">pad_sdf</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">ps</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span>
<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="n">ts_min</span><span class="p">,</span> <span class="n">ts_max</span><span class="p">])</span>
<span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="n">rule</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset</span><span class="o">.</span><span class="n">freqstr</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_closed</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_label</span><span class="p">)</span>
<span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="o">.</span><span class="n">index</span>
<span class="p">)</span>
<span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">spark_frame</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="n">SPARK_DEFAULT_INDEX_NAME</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="n">bin_col_name</span><span class="p">))</span>
<span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">ts_min</span> <span class="o">&lt;=</span> <span class="n">F</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="n">bin_col_name</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="n">bin_col_name</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">ts_max</span><span class="p">))</span>
<span class="p">)</span>
<span class="c1"># union the above two spark dataframes.</span>
<span class="n">sdf</span> <span class="o">=</span> <span class="n">bin_sdf</span><span class="o">.</span><span class="n">unionByName</span><span class="p">(</span><span class="n">pad_sdf</span><span class="p">,</span> <span class="n">allowMissingColumns</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
<span class="o">~</span><span class="n">F</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="n">bin_col_name</span><span class="p">))</span>
<span class="p">)</span>
<span class="n">internal</span> <span class="o">=</span> <span class="n">InternalFrame</span><span class="p">(</span>
<span class="n">spark_frame</span><span class="o">=</span><span class="n">sdf</span><span class="p">,</span>
<span class="n">index_spark_columns</span><span class="o">=</span><span class="p">[</span><span class="n">scol_for</span><span class="p">(</span><span class="n">sdf</span><span class="p">,</span> <span class="n">SPARK_DEFAULT_INDEX_NAME</span><span class="p">)],</span>
<span class="n">data_spark_columns</span><span class="o">=</span><span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="n">bin_col_name</span><span class="p">)]</span>
<span class="o">+</span> <span class="p">[</span><span class="n">scol_for</span><span class="p">(</span><span class="n">sdf</span><span class="p">,</span> <span class="n">psser</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">data_spark_column_names</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">psser</span> <span class="ow">in</span> <span class="n">agg_columns</span><span class="p">],</span>
<span class="n">column_labels</span><span class="o">=</span><span class="p">[</span><span class="n">bin_col_label</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">psser</span><span class="o">.</span><span class="n">_column_label</span> <span class="k">for</span> <span class="n">psser</span> <span class="ow">in</span> <span class="n">agg_columns</span><span class="p">],</span>
<span class="n">data_fields</span><span class="o">=</span><span class="p">[</span><span class="n">bin_col_field</span><span class="p">]</span>
<span class="o">+</span> <span class="p">[</span><span class="n">psser</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">data_fields</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">nullable</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">psser</span> <span class="ow">in</span> <span class="n">agg_columns</span><span class="p">],</span>
<span class="n">column_label_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_psdf</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">column_label_names</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">psdf</span><span class="p">:</span> <span class="n">DataFrame</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">internal</span><span class="p">)</span>
<span class="n">groupby</span> <span class="o">=</span> <span class="n">psdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">psdf</span><span class="o">.</span><span class="n">_psser_for</span><span class="p">(</span><span class="n">bin_col_label</span><span class="p">),</span> <span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">downsampled</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">groupby</span><span class="p">,</span> <span class="n">f</span><span class="p">)()</span>
<span class="n">downsampled</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">downsampled</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">psdf</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FrameLike</span><span class="p">:</span>
<span class="k">pass</span>
<div class="viewcode-block" id="Resampler.min"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.resample.Resampler.min.html#pyspark.pandas.resample.Resampler.min">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">min</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FrameLike</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute min of resampled values.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> pyspark.pandas.Series.groupby</span>
<span class="sd"> pyspark.pandas.DataFrame.groupby</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from datetime import datetime</span>
<span class="sd"> &gt;&gt;&gt; np.random.seed(22)</span>
<span class="sd"> &gt;&gt;&gt; dates = [</span>
<span class="sd"> ... datetime(2022, 5, 1, 4, 5, 6),</span>
<span class="sd"> ... datetime(2022, 5, 3),</span>
<span class="sd"> ... datetime(2022, 5, 3, 23, 59, 59),</span>
<span class="sd"> ... datetime(2022, 5, 4),</span>
<span class="sd"> ... pd.NaT,</span>
<span class="sd"> ... datetime(2022, 5, 4, 0, 0, 1),</span>
<span class="sd"> ... datetime(2022, 5, 11),</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; df = ps.DataFrame(</span>
<span class="sd"> ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates), columns=[&quot;A&quot;, &quot;B&quot;]</span>
<span class="sd"> ... )</span>
<span class="sd"> &gt;&gt;&gt; df</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 04:05:06 0.208461 0.481681</span>
<span class="sd"> 2022-05-03 00:00:00 0.420538 0.859182</span>
<span class="sd"> 2022-05-03 23:59:59 0.171162 0.338864</span>
<span class="sd"> 2022-05-04 00:00:00 0.270533 0.691041</span>
<span class="sd"> NaT 0.220405 0.811951</span>
<span class="sd"> 2022-05-04 00:00:01 0.010527 0.561204</span>
<span class="sd"> 2022-05-11 00:00:00 0.813726 0.745100</span>
<span class="sd"> &gt;&gt;&gt; df.resample(&quot;3D&quot;).min().sort_index()</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 0.171162 0.338864</span>
<span class="sd"> 2022-05-04 0.010527 0.561204</span>
<span class="sd"> 2022-05-07 NaN NaN</span>
<span class="sd"> 2022-05-10 0.813726 0.745100</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_downsample</span><span class="p">(</span><span class="s2">&quot;min&quot;</span><span class="p">))</span></div>
<div class="viewcode-block" id="Resampler.max"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.resample.Resampler.max.html#pyspark.pandas.resample.Resampler.max">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">max</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FrameLike</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute max of resampled values.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> pyspark.pandas.Series.groupby</span>
<span class="sd"> pyspark.pandas.DataFrame.groupby</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from datetime import datetime</span>
<span class="sd"> &gt;&gt;&gt; np.random.seed(22)</span>
<span class="sd"> &gt;&gt;&gt; dates = [</span>
<span class="sd"> ... datetime(2022, 5, 1, 4, 5, 6),</span>
<span class="sd"> ... datetime(2022, 5, 3),</span>
<span class="sd"> ... datetime(2022, 5, 3, 23, 59, 59),</span>
<span class="sd"> ... datetime(2022, 5, 4),</span>
<span class="sd"> ... pd.NaT,</span>
<span class="sd"> ... datetime(2022, 5, 4, 0, 0, 1),</span>
<span class="sd"> ... datetime(2022, 5, 11),</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; df = ps.DataFrame(</span>
<span class="sd"> ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates), columns=[&quot;A&quot;, &quot;B&quot;]</span>
<span class="sd"> ... )</span>
<span class="sd"> &gt;&gt;&gt; df</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 04:05:06 0.208461 0.481681</span>
<span class="sd"> 2022-05-03 00:00:00 0.420538 0.859182</span>
<span class="sd"> 2022-05-03 23:59:59 0.171162 0.338864</span>
<span class="sd"> 2022-05-04 00:00:00 0.270533 0.691041</span>
<span class="sd"> NaT 0.220405 0.811951</span>
<span class="sd"> 2022-05-04 00:00:01 0.010527 0.561204</span>
<span class="sd"> 2022-05-11 00:00:00 0.813726 0.745100</span>
<span class="sd"> &gt;&gt;&gt; df.resample(&quot;3D&quot;).max().sort_index()</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 0.420538 0.859182</span>
<span class="sd"> 2022-05-04 0.270533 0.691041</span>
<span class="sd"> 2022-05-07 NaN NaN</span>
<span class="sd"> 2022-05-10 0.813726 0.745100</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_downsample</span><span class="p">(</span><span class="s2">&quot;max&quot;</span><span class="p">))</span></div>
<div class="viewcode-block" id="Resampler.sum"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.resample.Resampler.sum.html#pyspark.pandas.resample.Resampler.sum">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FrameLike</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute sum of resampled values.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> pyspark.pandas.Series.groupby</span>
<span class="sd"> pyspark.pandas.DataFrame.groupby</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from datetime import datetime</span>
<span class="sd"> &gt;&gt;&gt; np.random.seed(22)</span>
<span class="sd"> &gt;&gt;&gt; dates = [</span>
<span class="sd"> ... datetime(2022, 5, 1, 4, 5, 6),</span>
<span class="sd"> ... datetime(2022, 5, 3),</span>
<span class="sd"> ... datetime(2022, 5, 3, 23, 59, 59),</span>
<span class="sd"> ... datetime(2022, 5, 4),</span>
<span class="sd"> ... pd.NaT,</span>
<span class="sd"> ... datetime(2022, 5, 4, 0, 0, 1),</span>
<span class="sd"> ... datetime(2022, 5, 11),</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; df = ps.DataFrame(</span>
<span class="sd"> ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates), columns=[&quot;A&quot;, &quot;B&quot;]</span>
<span class="sd"> ... )</span>
<span class="sd"> &gt;&gt;&gt; df</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 04:05:06 0.208461 0.481681</span>
<span class="sd"> 2022-05-03 00:00:00 0.420538 0.859182</span>
<span class="sd"> 2022-05-03 23:59:59 0.171162 0.338864</span>
<span class="sd"> 2022-05-04 00:00:00 0.270533 0.691041</span>
<span class="sd"> NaT 0.220405 0.811951</span>
<span class="sd"> 2022-05-04 00:00:01 0.010527 0.561204</span>
<span class="sd"> 2022-05-11 00:00:00 0.813726 0.745100</span>
<span class="sd"> &gt;&gt;&gt; df.resample(&quot;3D&quot;).sum().sort_index()</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 0.800160 1.679727</span>
<span class="sd"> 2022-05-04 0.281060 1.252245</span>
<span class="sd"> 2022-05-07 0.000000 0.000000</span>
<span class="sd"> 2022-05-10 0.813726 0.745100</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_downsample</span><span class="p">(</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mf">0.0</span><span class="p">))</span></div>
<div class="viewcode-block" id="Resampler.mean"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.resample.Resampler.mean.html#pyspark.pandas.resample.Resampler.mean">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FrameLike</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute mean of resampled values.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> pyspark.pandas.Series.groupby</span>
<span class="sd"> pyspark.pandas.DataFrame.groupby</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from datetime import datetime</span>
<span class="sd"> &gt;&gt;&gt; np.random.seed(22)</span>
<span class="sd"> &gt;&gt;&gt; dates = [</span>
<span class="sd"> ... datetime(2022, 5, 1, 4, 5, 6),</span>
<span class="sd"> ... datetime(2022, 5, 3),</span>
<span class="sd"> ... datetime(2022, 5, 3, 23, 59, 59),</span>
<span class="sd"> ... datetime(2022, 5, 4),</span>
<span class="sd"> ... pd.NaT,</span>
<span class="sd"> ... datetime(2022, 5, 4, 0, 0, 1),</span>
<span class="sd"> ... datetime(2022, 5, 11),</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; df = ps.DataFrame(</span>
<span class="sd"> ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates), columns=[&quot;A&quot;, &quot;B&quot;]</span>
<span class="sd"> ... )</span>
<span class="sd"> &gt;&gt;&gt; df</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 04:05:06 0.208461 0.481681</span>
<span class="sd"> 2022-05-03 00:00:00 0.420538 0.859182</span>
<span class="sd"> 2022-05-03 23:59:59 0.171162 0.338864</span>
<span class="sd"> 2022-05-04 00:00:00 0.270533 0.691041</span>
<span class="sd"> NaT 0.220405 0.811951</span>
<span class="sd"> 2022-05-04 00:00:01 0.010527 0.561204</span>
<span class="sd"> 2022-05-11 00:00:00 0.813726 0.745100</span>
<span class="sd"> &gt;&gt;&gt; df.resample(&quot;3D&quot;).mean().sort_index()</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 0.266720 0.559909</span>
<span class="sd"> 2022-05-04 0.140530 0.626123</span>
<span class="sd"> 2022-05-07 NaN NaN</span>
<span class="sd"> 2022-05-10 0.813726 0.745100</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_downsample</span><span class="p">(</span><span class="s2">&quot;mean&quot;</span><span class="p">))</span></div>
<div class="viewcode-block" id="Resampler.std"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.resample.Resampler.std.html#pyspark.pandas.resample.Resampler.std">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">std</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FrameLike</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute std of resampled values.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> pyspark.pandas.Series.groupby</span>
<span class="sd"> pyspark.pandas.DataFrame.groupby</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from datetime import datetime</span>
<span class="sd"> &gt;&gt;&gt; np.random.seed(22)</span>
<span class="sd"> &gt;&gt;&gt; dates = [</span>
<span class="sd"> ... datetime(2022, 5, 1, 4, 5, 6),</span>
<span class="sd"> ... datetime(2022, 5, 3),</span>
<span class="sd"> ... datetime(2022, 5, 3, 23, 59, 59),</span>
<span class="sd"> ... datetime(2022, 5, 4),</span>
<span class="sd"> ... pd.NaT,</span>
<span class="sd"> ... datetime(2022, 5, 4, 0, 0, 1),</span>
<span class="sd"> ... datetime(2022, 5, 11),</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; df = ps.DataFrame(</span>
<span class="sd"> ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates), columns=[&quot;A&quot;, &quot;B&quot;]</span>
<span class="sd"> ... )</span>
<span class="sd"> &gt;&gt;&gt; df</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 04:05:06 0.208461 0.481681</span>
<span class="sd"> 2022-05-03 00:00:00 0.420538 0.859182</span>
<span class="sd"> 2022-05-03 23:59:59 0.171162 0.338864</span>
<span class="sd"> 2022-05-04 00:00:00 0.270533 0.691041</span>
<span class="sd"> NaT 0.220405 0.811951</span>
<span class="sd"> 2022-05-04 00:00:01 0.010527 0.561204</span>
<span class="sd"> 2022-05-11 00:00:00 0.813726 0.745100</span>
<span class="sd"> &gt;&gt;&gt; df.resample(&quot;3D&quot;).std().sort_index()</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 0.134509 0.268835</span>
<span class="sd"> 2022-05-04 0.183852 0.091809</span>
<span class="sd"> 2022-05-07 NaN NaN</span>
<span class="sd"> 2022-05-10 NaN NaN</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_downsample</span><span class="p">(</span><span class="s2">&quot;std&quot;</span><span class="p">))</span></div>
<div class="viewcode-block" id="Resampler.var"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.resample.Resampler.var.html#pyspark.pandas.resample.Resampler.var">[docs]</a> <span class="k">def</span><span class="w"> </span><span class="nf">var</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FrameLike</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Compute var of resampled values.</span>
<span class="sd"> .. versionadded:: 3.4.0</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> pyspark.pandas.Series.groupby</span>
<span class="sd"> pyspark.pandas.DataFrame.groupby</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import numpy as np</span>
<span class="sd"> &gt;&gt;&gt; from datetime import datetime</span>
<span class="sd"> &gt;&gt;&gt; np.random.seed(22)</span>
<span class="sd"> &gt;&gt;&gt; dates = [</span>
<span class="sd"> ... datetime(2022, 5, 1, 4, 5, 6),</span>
<span class="sd"> ... datetime(2022, 5, 3),</span>
<span class="sd"> ... datetime(2022, 5, 3, 23, 59, 59),</span>
<span class="sd"> ... datetime(2022, 5, 4),</span>
<span class="sd"> ... pd.NaT,</span>
<span class="sd"> ... datetime(2022, 5, 4, 0, 0, 1),</span>
<span class="sd"> ... datetime(2022, 5, 11),</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; df = ps.DataFrame(</span>
<span class="sd"> ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates), columns=[&quot;A&quot;, &quot;B&quot;]</span>
<span class="sd"> ... )</span>
<span class="sd"> &gt;&gt;&gt; df</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 04:05:06 0.208461 0.481681</span>
<span class="sd"> 2022-05-03 00:00:00 0.420538 0.859182</span>
<span class="sd"> 2022-05-03 23:59:59 0.171162 0.338864</span>
<span class="sd"> 2022-05-04 00:00:00 0.270533 0.691041</span>
<span class="sd"> NaT 0.220405 0.811951</span>
<span class="sd"> 2022-05-04 00:00:01 0.010527 0.561204</span>
<span class="sd"> 2022-05-11 00:00:00 0.813726 0.745100</span>
<span class="sd"> &gt;&gt;&gt; df.resample(&quot;3D&quot;).var().sort_index()</span>
<span class="sd"> A B</span>
<span class="sd"> 2022-05-01 0.018093 0.072272</span>
<span class="sd"> 2022-05-04 0.033802 0.008429</span>
<span class="sd"> 2022-05-07 NaN NaN</span>
<span class="sd"> 2022-05-10 NaN NaN</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_downsample</span><span class="p">(</span><span class="s2">&quot;var&quot;</span><span class="p">))</span></div>
<span class="k">class</span><span class="w"> </span><span class="nc">DataFrameResampler</span><span class="p">(</span><span class="n">Resampler</span><span class="p">[</span><span class="n">DataFrame</span><span class="p">]):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">psdf</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span>
<span class="n">resamplekey</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Series</span><span class="p">],</span>
<span class="n">rule</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">closed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">label</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">agg_columns</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Series</span><span class="p">]</span> <span class="o">=</span> <span class="p">[],</span>
<span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">psdf</span><span class="o">=</span><span class="n">psdf</span><span class="p">,</span>
<span class="n">resamplekey</span><span class="o">=</span><span class="n">resamplekey</span><span class="p">,</span>
<span class="n">rule</span><span class="o">=</span><span class="n">rule</span><span class="p">,</span>
<span class="n">closed</span><span class="o">=</span><span class="n">closed</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
<span class="n">agg_columns</span><span class="o">=</span><span class="n">agg_columns</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">MissingPandasLikeDataFrameResampler</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
<span class="n">property_or_func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">MissingPandasLikeDataFrameResampler</span><span class="p">,</span> <span class="n">item</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">property_or_func</span><span class="p">,</span> <span class="nb">property</span><span class="p">):</span>
<span class="k">return</span> <span class="n">property_or_func</span><span class="o">.</span><span class="n">fget</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">partial</span><span class="p">(</span><span class="n">property_or_func</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">psdf</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="k">return</span> <span class="n">psdf</span>
<span class="k">class</span><span class="w"> </span><span class="nc">SeriesResampler</span><span class="p">(</span><span class="n">Resampler</span><span class="p">[</span><span class="n">Series</span><span class="p">]):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">psser</span><span class="p">:</span> <span class="n">Series</span><span class="p">,</span>
<span class="n">resamplekey</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Series</span><span class="p">],</span>
<span class="n">rule</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">closed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">label</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">agg_columns</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Series</span><span class="p">]</span> <span class="o">=</span> <span class="p">[],</span>
<span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">psdf</span><span class="o">=</span><span class="n">psser</span><span class="o">.</span><span class="n">_psdf</span><span class="p">,</span>
<span class="n">resamplekey</span><span class="o">=</span><span class="n">resamplekey</span><span class="p">,</span>
<span class="n">rule</span><span class="o">=</span><span class="n">rule</span><span class="p">,</span>
<span class="n">closed</span><span class="o">=</span><span class="n">closed</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span>
<span class="n">agg_columns</span><span class="o">=</span><span class="n">agg_columns</span><span class="p">,</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_psser</span> <span class="o">=</span> <span class="n">psser</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">MissingPandasLikeSeriesResampler</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
<span class="n">property_or_func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">MissingPandasLikeSeriesResampler</span><span class="p">,</span> <span class="n">item</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">property_or_func</span><span class="p">,</span> <span class="nb">property</span><span class="p">):</span>
<span class="k">return</span> <span class="n">property_or_func</span><span class="o">.</span><span class="n">fget</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">partial</span><span class="p">(</span><span class="n">property_or_func</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_handle_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">psdf</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Series</span><span class="p">:</span>
<span class="k">return</span> <span class="n">first_series</span><span class="p">(</span><span class="n">psdf</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_psser</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_test</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">doctest</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">sys</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">SparkSession</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pyspark.pandas.resample</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SPARK_HOME&quot;</span><span class="p">])</span>
<span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span><span class="o">.</span><span class="n">resample</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">globs</span><span class="p">[</span><span class="s2">&quot;ps&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span>
<span class="n">spark</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">&quot;local[4]&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">&quot;pyspark.pandas.resample tests&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="p">)</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span>
<span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span><span class="o">.</span><span class="n">resample</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="p">,</span>
<span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="n">_test</span><span class="p">()</span>
</pre></div>
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