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   <div class="section" id="pyspark-sql-dataframe-approxquantile">
 <h1>pyspark.sql.DataFrame.approxQuantile<a class="headerlink" href="#pyspark-sql-dataframe-approxquantile" title="Permalink to this headline">¶</a></h1>
 <dl class="py method">
 <dt id="pyspark.sql.DataFrame.approxQuantile">
 <code class="sig-prename descclassname">DataFrame.</code><code class="sig-name descname">approxQuantile</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">col</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span>List<span class="p">[</span>str<span class="p">]</span><span class="p">, </span>Tuple<span class="p">[</span>str<span class="p">]</span><span class="p">]</span></span></em>, <em class="sig-param"><span class="n">probabilities</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>List<span class="p">[</span>float<span class="p">]</span><span class="p">, </span>Tuple<span class="p">[</span>float<span class="p">]</span><span class="p">]</span></span></em>, <em class="sig-param"><span class="n">relativeError</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span> &#x2192; Union<span class="p">[</span>List<span class="p">[</span>float<span class="p">]</span><span class="p">, </span>List<span class="p">[</span>List<span class="p">[</span>float<span class="p">]</span><span class="p">]</span><span class="p">]</span><a class="reference internal" href="../../../_modules/pyspark/sql/dataframe.html#DataFrame.approxQuantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.sql.DataFrame.approxQuantile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculates the approximate quantiles of numerical columns of a
 <a class="reference internal" href="pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a>.</p>
 <p>The result of this algorithm has the following deterministic bound:
 If the <a class="reference internal" href="pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a> has N elements and if we request the quantile at
 probability <cite>p</cite> up to error <cite>err</cite>, then the algorithm will return
 a sample <cite>x</cite> from the <a class="reference internal" href="pyspark.sql.DataFrame.html#pyspark.sql.DataFrame" title="pyspark.sql.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a> so that the <em>exact</em> rank of <cite>x</cite> is
 close to (p * N). More precisely,</p>
 <blockquote>
 <div><p>floor((p - err) * N) &lt;= rank(x) &lt;= ceil((p + err) * N).</p>
 </div></blockquote>
 <p>This method implements a variation of the Greenwald-Khanna
 algorithm (with some speed optimizations). The algorithm was first
 present in [[<a class="reference external" href="https://doi.org/10.1145/375663.375670">https://doi.org/10.1145/375663.375670</a>
 Space-efficient Online Computation of Quantile Summaries]]
 by Greenwald and Khanna.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">New in version 2.0.0.</span></p>
 </div>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 3.4.0: </span>Supports Spark Connect.</p>
 </div>
 <dl class="field-list">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><dl>
 <dt><strong>col: str, tuple or list</strong></dt><dd><p>Can be a single column name, or a list of names for multiple columns.</p>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 2.2.0: </span>Added support for multiple columns.</p>
 </div>
 </dd>
 <dt><strong>probabilities</strong><span class="classifier">list or tuple</span></dt><dd><p>a list of quantile probabilities
 Each number must belong to [0, 1].
 For example 0 is the minimum, 0.5 is the median, 1 is the maximum.</p>
 </dd>
 <dt><strong>relativeError</strong><span class="classifier">float</span></dt><dd><p>The relative target precision to achieve
 (&gt;= 0). If set to zero, the exact quantiles are computed, which
 could be very expensive. Note that values greater than 1 are
 accepted but gives the same result as 1.</p>
 </dd>
 </dl>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><dl class="simple">
 <dt>list</dt><dd><p>the approximate quantiles at the given probabilities.</p>
 <ul class="simple">
 <li><p>If the input <cite>col</cite> is a string, the output is a list of floats.</p></li>
 <li><dl class="simple">
 <dt>If the input <cite>col</cite> is a list or tuple of strings, the output is also a</dt><dd><p>list, but each element in it is a list of floats, i.e., the output
 is a list of list of floats.</p>
 </dd>
 </dl>
 </li>
 </ul>
 </dd>
 </dl>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>Null values will be ignored in numerical columns before calculation.
 For columns only containing null values, an empty list is returned.</p>
 </dd></dl>

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