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<div class="section" id="module-apache_beam.dataframe.convert">
<span id="apache-beam-dataframe-convert-module"></span><h1>apache_beam.dataframe.convert module<a class="headerlink" href="#module-apache_beam.dataframe.convert" title="Permalink to this headline"></a></h1>
<dl class="function">
<dt id="apache_beam.dataframe.convert.to_dataframe">
<code class="descclassname">apache_beam.dataframe.convert.</code><code class="descname">to_dataframe</code><span class="sig-paren">(</span><em>pcoll</em>, <em>proxy=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/convert.html#to_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.convert.to_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Converts a PCollection to a deferred dataframe-like object, which can
manipulated with pandas methods like <cite>filter</cite> and <cite>groupby</cite>.</p>
<p>For example, one might write:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">=</span> <span class="o">...</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">to_dataframe</span><span class="p">(</span><span class="n">pcoll</span><span class="p">,</span> <span class="n">proxy</span><span class="o">=...</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;col&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">pcoll_result</span> <span class="o">=</span> <span class="n">to_pcollection</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</pre></div>
</div>
<p>A proxy object must be given if the schema for the PCollection is not known.</p>
</dd></dl>
<dl class="function">
<dt id="apache_beam.dataframe.convert.to_pcollection">
<code class="descclassname">apache_beam.dataframe.convert.</code><code class="descname">to_pcollection</code><span class="sig-paren">(</span><em>*dataframes</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/dataframe/convert.html#to_pcollection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.dataframe.convert.to_pcollection" title="Permalink to this definition"></a></dt>
<dd><p>Converts one or more deferred dataframe-like objects back to a PCollection.</p>
<p>This method creates and applies the actual Beam operations that compute
the given deferred dataframes, returning a PCollection of their results. By
default the resulting PCollections are schema-aware PCollections where each
element is one row from the output dataframes, excluding indexes. This
behavior can be modified with the <cite>yield_elements</cite> and <cite>include_indexes</cite>
arguments.</p>
<p>If more than one (related) result is desired, it can be more efficient to
pass them all at the same time to this method.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>always_return_tuple</strong> – (optional, default: False) If true, always return
a tuple of PCollections, even if there’s only one output.</li>
<li><strong>yield_elements</strong> – (optional, default: “schemas”) If set to “pandas”, return
PCollections containing the raw Pandas objects (DataFrames or Series),
if set to “schemas”, return an element-wise PCollection, where DataFrame
and Series instances are expanded to one element per row. DataFrames are
converted to schema-aware PCollections, where column values can be
accessed by attribute.</li>
<li><strong>include_indexes</strong> – (optional, default: False) When yield_elements=”schemas”,
if include_indexes=True, attempt to include index columns in the output
schema for expanded DataFrames. Raises an error if any of the index
levels are unnamed (name=None), or if any of the names are not unique
among all column and index names.</li>
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