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| <div class="section" id="pyspark-pandas-dataframe-cummin"> |
| <h1>pyspark.pandas.DataFrame.cummin<a class="headerlink" href="#pyspark-pandas-dataframe-cummin" title="Permalink to this headline">¶</a></h1> |
| <dl class="py method"> |
| <dt id="pyspark.pandas.DataFrame.cummin"> |
| <code class="sig-prename descclassname">DataFrame.</code><code class="sig-name descname">cummin</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">skipna</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">True</span></em><span class="sig-paren">)</span> → FrameLike<a class="headerlink" href="#pyspark.pandas.DataFrame.cummin" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return cumulative minimum over a DataFrame or Series axis.</p> |
| <p>Returns a DataFrame or Series of the same size containing the cumulative minimum.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>the current implementation of cummin uses Spark’s Window without |
| specifying partition specification. This leads to move all data into |
| single partition in single machine and could cause serious |
| performance degradation. Avoid this method against very large dataset.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>skipna</strong><span class="classifier">boolean, default True</span></dt><dd><p>Exclude NA/null values. If an entire row/column is NA, the result will be NA.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt>DataFrame or Series</dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="pyspark.pandas.DataFrame.min.html#pyspark.pandas.DataFrame.min" title="pyspark.pandas.DataFrame.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame.min</span></code></a></dt><dd><p>Return the minimum over DataFrame axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.DataFrame.cummax.html#pyspark.pandas.DataFrame.cummax" title="pyspark.pandas.DataFrame.cummax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame.cummax</span></code></a></dt><dd><p>Return cumulative maximum over DataFrame axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="#pyspark.pandas.DataFrame.cummin" title="pyspark.pandas.DataFrame.cummin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame.cummin</span></code></a></dt><dd><p>Return cumulative minimum over DataFrame axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.DataFrame.cumsum.html#pyspark.pandas.DataFrame.cumsum" title="pyspark.pandas.DataFrame.cumsum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame.cumsum</span></code></a></dt><dd><p>Return cumulative sum over DataFrame axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.Series.min.html#pyspark.pandas.Series.min" title="pyspark.pandas.Series.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Series.min</span></code></a></dt><dd><p>Return the minimum over Series axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.Series.cummax.html#pyspark.pandas.Series.cummax" title="pyspark.pandas.Series.cummax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Series.cummax</span></code></a></dt><dd><p>Return cumulative maximum over Series axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.Series.cummin.html#pyspark.pandas.Series.cummin" title="pyspark.pandas.Series.cummin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Series.cummin</span></code></a></dt><dd><p>Return cumulative minimum over Series axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.Series.cumsum.html#pyspark.pandas.Series.cumsum" title="pyspark.pandas.Series.cumsum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Series.cumsum</span></code></a></dt><dd><p>Return cumulative sum over Series axis.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.Series.cumprod.html#pyspark.pandas.Series.cumprod" title="pyspark.pandas.Series.cumprod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Series.cumprod</span></code></a></dt><dd><p>Return cumulative product over Series axis.</p> |
| </dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]],</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'AB'</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> A B</span> |
| <span class="go">0 2.0 1.0</span> |
| <span class="go">1 3.0 NaN</span> |
| <span class="go">2 1.0 0.0</span> |
| </pre></div> |
| </div> |
| <p>By default, iterates over rows and finds the minimum in each column.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">cummin</span><span class="p">()</span> |
| <span class="go"> A B</span> |
| <span class="go">0 2.0 1.0</span> |
| <span class="go">1 2.0 NaN</span> |
| <span class="go">2 1.0 0.0</span> |
| </pre></div> |
| </div> |
| <p>It works identically in Series.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">cummin</span><span class="p">()</span> |
| <span class="go">0 2.0</span> |
| <span class="go">1 2.0</span> |
| <span class="go">2 1.0</span> |
| <span class="go">Name: A, dtype: float64</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
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