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<li class="toctree-l4"><a class="reference internal" href="#apache_beam.transforms.stats.ApproximateQuantiles"><code class="docutils literal notranslate"><span class="pre">ApproximateQuantiles</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#apache_beam.transforms.stats.ApproximateUnique"><code class="docutils literal notranslate"><span class="pre">ApproximateUnique</span></code></a></li>
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<section id="module-apache_beam.transforms.stats">
<span id="apache-beam-transforms-stats-module"></span><h1>apache_beam.transforms.stats module<a class="headerlink" href="#module-apache_beam.transforms.stats" title="Link to this heading"></a></h1>
<p>This module has all statistic related transforms.</p>
<p>This ApproximateUnique class will be deprecated [1]. PLease look into using
HLLCount in the zetasketch extension module [2].</p>
<p>[1] <a class="reference external" href="https://lists.apache.org/thread.html/501605df5027567099b81f18c080469661fb426">https://lists.apache.org/thread.html/501605df5027567099b81f18c080469661fb426</a>
4a002615fa1510502%40%3Cdev.beam.apache.org%3E
[2] <a class="reference external" href="https://beam.apache.org/releases/javadoc/2.16.0/org/apache/beam/sdk/extensio">https://beam.apache.org/releases/javadoc/2.16.0/org/apache/beam/sdk/extensio</a>
ns/zetasketch/HllCount.html</p>
<dl class="py class">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateQuantiles">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">apache_beam.transforms.stats.</span></span><span class="sig-name descname"><span class="pre">ApproximateQuantiles</span></span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
<p>PTransform for getting the idea of data distribution using approximate N-tile
(e.g. quartiles, percentiles etc.) either globally or per-key.</p>
<p class="rubric">Examples</p>
<p>in: list(range(101)), num_quantiles=5</p>
<p>out: [0, 25, 50, 75, 100]</p>
<dl class="simple">
<dt>in: [(i, 1 if i&lt;10 else 1e-5) for i in range(101)], num_quantiles=5,</dt><dd><p>weighted=True</p>
</dd>
</dl>
<p>out: [0, 2, 5, 7, 100]</p>
<dl class="simple">
<dt>in: [list(range(10)), …, list(range(90, 101))], num_quantiles=5,</dt><dd><p>input_batched=True</p>
</dd>
</dl>
<p>out: [0, 25, 50, 75, 100]</p>
<dl class="simple">
<dt>in: [(list(range(10)), [1]*10), (list(range(10)), [0]*10), …,</dt><dd><p>(list(range(90, 101)), [0]*11)], num_quantiles=5, input_batched=True,
weighted=True</p>
</dd>
</dl>
<p>out: [0, 2, 5, 7, 100]</p>
<dl class="py class">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateQuantiles.Globally">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Globally</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_quantiles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weighted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_batched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.Globally" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">PTransform</span></code></a></p>
<p>PTransform takes PCollection and returns a list whose single value is
approximate N-tiles of the input collection globally.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>num_quantiles</strong> – number of elements in the resulting quantiles values list.</p></li>
<li><p><strong>key</strong> – (optional) Key is a mapping of elements to a comparable key, similar
to the key argument of Python’s sorting methods.</p></li>
<li><p><strong>reverse</strong> – (optional) whether to order things smallest to largest, rather
than largest to smallest.</p></li>
<li><p><strong>weighted</strong> – (optional) if set to True, the transform returns weighted
quantiles. The input PCollection is then expected to contain tuples of
input values with the corresponding weight.</p></li>
<li><p><strong>input_batched</strong> – (optional) if set to True, the transform expects each
element of input PCollection to be a batch, which is a list of elements
for non-weighted case and a tuple of lists of elements and weights for
weighted. Provides a way to accumulate multiple elements at a time more
efficiently.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateQuantiles.Globally.display_data">
<span class="sig-name descname"><span class="pre">display_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.Globally.display_data" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateQuantiles.Globally.expand">
<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pcoll</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.Globally.expand" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateQuantiles.PerKey">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">PerKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_quantiles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weighted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_batched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.PerKey" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">PTransform</span></code></a></p>
<p>PTransform takes PCollection of KV and returns a list based on each key
whose single value is list of approximate N-tiles of the input element of
the key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>num_quantiles</strong> – number of elements in the resulting quantiles values list.</p></li>
<li><p><strong>key</strong> – (optional) Key is a mapping of elements to a comparable key, similar
to the key argument of Python’s sorting methods.</p></li>
<li><p><strong>reverse</strong> – (optional) whether to order things smallest to largest, rather
than largest to smallest.</p></li>
<li><p><strong>weighted</strong> – (optional) if set to True, the transform returns weighted
quantiles. The input PCollection is then expected to contain tuples of
input values with the corresponding weight.</p></li>
<li><p><strong>input_batched</strong> – (optional) if set to True, the transform expects each
element of input PCollection to be a batch, which is a list of elements
for non-weighted case and a tuple of lists of elements and weights for
weighted. Provides a way to accumulate multiple elements at a time more
efficiently.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateQuantiles.PerKey.display_data">
<span class="sig-name descname"><span class="pre">display_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.PerKey.display_data" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateQuantiles.PerKey.expand">
<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pcoll</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateQuantiles.PerKey.expand" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateUnique">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">apache_beam.transforms.stats.</span></span><span class="sig-name descname"><span class="pre">ApproximateUnique</span></span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
<p>Hashes input elements and uses those to extrapolate the size of the entire
set of hash values by assuming the rest of the hash values are as densely
distributed as the sample space.</p>
<dl class="py class">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateUnique.Globally">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Globally</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.Globally" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">PTransform</span></code></a></p>
<p>Approximate.Globally approximate number of unique values</p>
<dl class="py method">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateUnique.Globally.expand">
<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pcoll</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.Globally.expand" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateUnique.PerKey">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">PerKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.PerKey" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">PTransform</span></code></a></p>
<p>Approximate.PerKey approximate number of unique values per key</p>
<dl class="py method">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateUnique.PerKey.expand">
<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pcoll</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.PerKey.expand" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="apache_beam.transforms.stats.ApproximateUnique.parse_input_params">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">parse_input_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#apache_beam.transforms.stats.ApproximateUnique.parse_input_params" title="Link to this definition"></a></dt>
<dd><p>Check if input params are valid and return sample size.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>size</strong> – an int not smaller than 16, which we would use to estimate
number of unique values.</p></li>
<li><p><strong>error</strong> – max estimation error, which is a float between 0.01 and 0.50.
If error is given, sample size will be calculated from error with
_get_sample_size_from_est_error function.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>sample size</p>
</dd>
<dt class="field-odd">Raises<span class="colon">:</span></dt>
<dd class="field-odd"><p>ValueError: If both size and error are given, or neither is given, or
values are out of range.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
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