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| <section id="pyarrow-chunkedarray"> |
| <h1>pyarrow.ChunkedArray<a class="headerlink" href="#pyarrow-chunkedarray" title="Permalink to this heading">#</a></h1> |
| <dl class="py class"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray"> |
| <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyarrow.</span></span><span class="sig-name descname"><span class="pre">ChunkedArray</span></span><a class="headerlink" href="#pyarrow.ChunkedArray" title="Permalink to this definition">#</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">_PandasConvertible</span></code></p> |
| <p>An array-like composed from a (possibly empty) collection of pyarrow.Arrays</p> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>Do not call this class’s constructor directly.</p> |
| </div> |
| <p class="rubric">Examples</p> |
| <p>To construct a ChunkedArray object use <a class="reference internal" href="pyarrow.chunked_array.html#pyarrow.chunked_array" title="pyarrow.chunked_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.chunked_array()</span></code></a>:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">int8</span><span class="p">())</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go">...</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]]),</span> <span class="n">pa</span><span class="o">.</span><span class="n">ChunkedArray</span><span class="p">)</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.__init__"> |
| <span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.__init__" title="Permalink to this definition">#</a></dt> |
| <dd></dd></dl> |
| |
| <p class="rubric">Methods</p> |
| <table class="autosummary longtable table autosummary"> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.__init__" title="pyarrow.ChunkedArray.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(*args, **kwargs)</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.cast" title="pyarrow.ChunkedArray.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(self[, target_type, safe, options])</p></td> |
| <td><p>Cast array values to another data type</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.chunk" title="pyarrow.ChunkedArray.chunk"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chunk</span></code></a>(self, i)</p></td> |
| <td><p>Select a chunk by its index.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.combine_chunks" title="pyarrow.ChunkedArray.combine_chunks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">combine_chunks</span></code></a>(self, MemoryPool memory_pool=None)</p></td> |
| <td><p>Flatten this ChunkedArray into a single non-chunked array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.dictionary_encode" title="pyarrow.ChunkedArray.dictionary_encode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary_encode</span></code></a>(self[, null_encoding])</p></td> |
| <td><p>Compute dictionary-encoded representation of array.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.drop_null" title="pyarrow.ChunkedArray.drop_null"><code class="xref py py-obj docutils literal notranslate"><span class="pre">drop_null</span></code></a>(self)</p></td> |
| <td><p>Remove missing values from a chunked array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.equals" title="pyarrow.ChunkedArray.equals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">equals</span></code></a>(self, ChunkedArray other)</p></td> |
| <td><p>Return whether the contents of two chunked arrays are equal.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.fill_null" title="pyarrow.ChunkedArray.fill_null"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fill_null</span></code></a>(self, fill_value)</p></td> |
| <td><p>Replace each null element in values with fill_value.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.filter" title="pyarrow.ChunkedArray.filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filter</span></code></a>(self, mask[, null_selection_behavior])</p></td> |
| <td><p>Select values from the chunked array.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.flatten" title="pyarrow.ChunkedArray.flatten"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flatten</span></code></a>(self, MemoryPool memory_pool=None)</p></td> |
| <td><p>Flatten this ChunkedArray.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.format" title="pyarrow.ChunkedArray.format"><code class="xref py py-obj docutils literal notranslate"><span class="pre">format</span></code></a>(self, **kwargs)</p></td> |
| <td><p>DEPRECATED, use pyarrow.ChunkedArray.to_string</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.get_total_buffer_size" title="pyarrow.ChunkedArray.get_total_buffer_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_total_buffer_size</span></code></a>(self)</p></td> |
| <td><p>The sum of bytes in each buffer referenced by the chunked array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.index" title="pyarrow.ChunkedArray.index"><code class="xref py py-obj docutils literal notranslate"><span class="pre">index</span></code></a>(self, value[, start, end, memory_pool])</p></td> |
| <td><p>Find the first index of a value.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.is_nan" title="pyarrow.ChunkedArray.is_nan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_nan</span></code></a>(self)</p></td> |
| <td><p>Return boolean array indicating the NaN values.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.is_null" title="pyarrow.ChunkedArray.is_null"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_null</span></code></a>(self, *[, nan_is_null])</p></td> |
| <td><p>Return boolean array indicating the null values.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.is_valid" title="pyarrow.ChunkedArray.is_valid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_valid</span></code></a>(self)</p></td> |
| <td><p>Return boolean array indicating the non-null values.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.iterchunks" title="pyarrow.ChunkedArray.iterchunks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iterchunks</span></code></a>(self)</p></td> |
| <td><p>Convert to an iterator of ChunkArrays.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.length" title="pyarrow.ChunkedArray.length"><code class="xref py py-obj docutils literal notranslate"><span class="pre">length</span></code></a>(self)</p></td> |
| <td><p>Return length of a ChunkedArray.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.slice" title="pyarrow.ChunkedArray.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(self[, offset, length])</p></td> |
| <td><p>Compute zero-copy slice of this ChunkedArray</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.sort" title="pyarrow.ChunkedArray.sort"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sort</span></code></a>(self[, order])</p></td> |
| <td><p>Sort the ChunkedArray</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.take" title="pyarrow.ChunkedArray.take"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take</span></code></a>(self, indices)</p></td> |
| <td><p>Select values from the chunked array.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.to_numpy" title="pyarrow.ChunkedArray.to_numpy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_numpy</span></code></a>(self[, zero_copy_only])</p></td> |
| <td><p>Return a NumPy copy of this array (experimental).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.to_pandas" title="pyarrow.ChunkedArray.to_pandas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_pandas</span></code></a>(self[, memory_pool, categories, ...])</p></td> |
| <td><p>Convert to a pandas-compatible NumPy array or DataFrame, as appropriate</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.to_pylist" title="pyarrow.ChunkedArray.to_pylist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_pylist</span></code></a>(self)</p></td> |
| <td><p>Convert to a list of native Python objects.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.to_string" title="pyarrow.ChunkedArray.to_string"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_string</span></code></a>(self, *, int indent=0, ...)</p></td> |
| <td><p>Render a "pretty-printed" string representation of the ChunkedArray</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.unify_dictionaries" title="pyarrow.ChunkedArray.unify_dictionaries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unify_dictionaries</span></code></a>(self, ...)</p></td> |
| <td><p>Unify dictionaries across all chunks.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.unique" title="pyarrow.ChunkedArray.unique"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unique</span></code></a>(self)</p></td> |
| <td><p>Compute distinct elements in array</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.validate" title="pyarrow.ChunkedArray.validate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">validate</span></code></a>(self, *[, full])</p></td> |
| <td><p>Perform validation checks.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.value_counts" title="pyarrow.ChunkedArray.value_counts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">value_counts</span></code></a>(self)</p></td> |
| <td><p>Compute counts of unique elements in array.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Attributes</p> |
| <table class="autosummary longtable table autosummary"> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.chunks" title="pyarrow.ChunkedArray.chunks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chunks</span></code></a></p></td> |
| <td><p>Convert to a list of single-chunked arrays.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.data" title="pyarrow.ChunkedArray.data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">data</span></code></a></p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.nbytes" title="pyarrow.ChunkedArray.nbytes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nbytes</span></code></a></p></td> |
| <td><p>Total number of bytes consumed by the elements of the chunked array.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.null_count" title="pyarrow.ChunkedArray.null_count"><code class="xref py py-obj docutils literal notranslate"><span class="pre">null_count</span></code></a></p></td> |
| <td><p>Number of null entries</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.num_chunks" title="pyarrow.ChunkedArray.num_chunks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_chunks</span></code></a></p></td> |
| <td><p>Number of underlying chunks.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.ChunkedArray.type" title="pyarrow.ChunkedArray.type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code></a></p></td> |
| <td><p>Return data type of a ChunkedArray.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.cast"> |
| <span class="sig-name descname"><span class="pre">cast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</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">safe</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">options</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="#pyarrow.ChunkedArray.cast" title="Permalink to this definition">#</a></dt> |
| <dd><p>Cast array values to another data type</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.cast.html#pyarrow.compute.cast" title="pyarrow.compute.cast"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.cast()</span></code></a> for usage.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>target_type</strong><span class="classifier"><a class="reference internal" href="pyarrow.DataType.html#pyarrow.DataType" title="pyarrow.DataType"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataType</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Type to cast array to.</p> |
| </dd> |
| <dt><strong>safe</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Whether to check for conversion errors such as overflow.</p> |
| </dd> |
| <dt><strong>options</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastOptions</span></code>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Additional checks pass by CastOptions</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>cast</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">type</span> |
| <span class="go">DataType(int64)</span> |
| </pre></div> |
| </div> |
| <p>Change the data type of an array:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">n_legs_seconds</span> <span class="o">=</span> <span class="n">n_legs</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">duration</span><span class="p">(</span><span class="s1">'s'</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">n_legs_seconds</span><span class="o">.</span><span class="n">type</span> |
| <span class="go">DurationType(duration[s])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.chunk"> |
| <span class="sig-name descname"><span class="pre">chunk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.chunk" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select a chunk by its index.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>i</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Array</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.Int64Array object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.chunks"> |
| <span class="sig-name descname"><span class="pre">chunks</span></span><a class="headerlink" href="#pyarrow.ChunkedArray.chunks" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert to a list of single-chunked arrays.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> null</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">chunks</span> |
| <span class="go">[<pyarrow.lib.Int64Array object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> null</span> |
| <span class="go">], <pyarrow.lib.Int64Array object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go">]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.combine_chunks"> |
| <span class="sig-name descname"><span class="pre">combine_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MemoryPool</span> <span class="pre">memory_pool=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.combine_chunks" title="Permalink to this definition">#</a></dt> |
| <dd><p>Flatten this ChunkedArray into a single non-chunked array.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>For memory allocations, if required, otherwise use default pool</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>result</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">combine_chunks</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.Int64Array object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.data"> |
| <span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#pyarrow.ChunkedArray.data" title="Permalink to this definition">#</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.dictionary_encode"> |
| <span class="sig-name descname"><span class="pre">dictionary_encode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">null_encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mask'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.dictionary_encode" title="Permalink to this definition">#</a></dt> |
| <dd><p>Compute dictionary-encoded representation of array.</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.dictionary_encode.html#pyarrow.compute.dictionary_encode" title="pyarrow.compute.dictionary_encode"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.dictionary_encode()</span></code></a> for full usage.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>null_encoding</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, default “mask”</span></dt><dd><p>How to handle null entries.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>encoded</strong><span class="classifier"><a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd><p>A dictionary-encoded version of this array.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">((</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]</span> |
| <span class="gp">... </span> <span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">animals</span><span class="o">.</span><span class="n">dictionary_encode</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go">...</span> |
| <span class="go"> -- dictionary:</span> |
| <span class="go"> [</span> |
| <span class="go"> "Flamingo",</span> |
| <span class="go"> "Parrot",</span> |
| <span class="go"> "Dog",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> "Brittle stars",</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go"> -- indices:</span> |
| <span class="go"> [</span> |
| <span class="go"> 0,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2</span> |
| <span class="go"> ],</span> |
| <span class="go">...</span> |
| <span class="go"> -- dictionary:</span> |
| <span class="go"> [</span> |
| <span class="go"> "Flamingo",</span> |
| <span class="go"> "Parrot",</span> |
| <span class="go"> "Dog",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> "Brittle stars",</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go"> -- indices:</span> |
| <span class="go"> [</span> |
| <span class="go"> 3,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.drop_null"> |
| <span class="sig-name descname"><span class="pre">drop_null</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.drop_null" title="Permalink to this definition">#</a></dt> |
| <dd><p>Remove missing values from a chunked array. |
| See <a class="reference internal" href="pyarrow.compute.drop_null.html#pyarrow.compute.drop_null" title="pyarrow.compute.drop_null"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.drop_null()</span></code></a> for full description.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> null</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">drop_null</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.equals"> |
| <span class="sig-name descname"><span class="pre">equals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ChunkedArray</span> <span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.equals" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return whether the contents of two chunked arrays are equal.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>other</strong><span class="classifier"><a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.ChunkedArray</span></code></a></span></dt><dd><p>Chunked array to compare against.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>are_equal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">((</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]</span> |
| <span class="gp">... </span> <span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">n_legs</span><span class="p">)</span> |
| <span class="go">True</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">animals</span><span class="p">)</span> |
| <span class="go">False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.fill_null"> |
| <span class="sig-name descname"><span class="pre">fill_null</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.fill_null" title="Permalink to this definition">#</a></dt> |
| <dd><p>Replace each null element in values with fill_value.</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.fill_null.html#pyarrow.compute.fill_null" title="pyarrow.compute.fill_null"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.fill_null()</span></code></a> for full usage.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>fill_value</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#any" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">any</span></code></a></span></dt><dd><p>The replacement value for null entries.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>result</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd><p>A new array with nulls replaced by the given value.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">fill_value</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">int8</span><span class="p">())</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">fill_null</span><span class="p">(</span><span class="n">fill_value</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.filter"> |
| <span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">null_selection_behavior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'drop'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.filter" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select values from the chunked array.</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.filter.html#pyarrow.compute.filter" title="pyarrow.compute.filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.filter()</span></code></a> for full usage.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>mask</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="pyarrow.array.html#pyarrow.array" title="pyarrow.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">array-like</span></code></a></span></dt><dd><p>The boolean mask to filter the chunked array with.</p> |
| </dd> |
| <dt><strong>null_selection_behavior</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, default “drop”</span></dt><dd><p>How nulls in the mask should be handled.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>filtered</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd><p>An array of the same type, with only the elements selected by |
| the boolean mask.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">mask</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">null_selection_behavior</span><span class="o">=</span><span class="s2">"emit_null"</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> null</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.flatten"> |
| <span class="sig-name descname"><span class="pre">flatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MemoryPool</span> <span class="pre">memory_pool=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.flatten" title="Permalink to this definition">#</a></dt> |
| <dd><p>Flatten this ChunkedArray. If it has a struct type, the column is |
| flattened into one array per struct field.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>For memory allocations, if required, otherwise use default pool</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>result</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">c_arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">(</span><span class="n">n_legs</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span> |
| <span class="gp">>>> </span><span class="n">c_arr</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> -- is_valid: all not null</span> |
| <span class="go"> -- child 0 type: int64</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go"> -- child 1 type: int64</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 1</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">c_arr</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> |
| <span class="go">[<pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">], <pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 1</span> |
| <span class="go"> ]</span> |
| <span class="go">]]</span> |
| <span class="gp">>>> </span><span class="n">c_arr</span><span class="o">.</span><span class="n">type</span> |
| <span class="go">StructType(struct<values: int64, counts: int64>)</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">type</span> |
| <span class="go">DataType(int64)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.format"> |
| <span class="sig-name descname"><span class="pre">format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.format" title="Permalink to this definition">#</a></dt> |
| <dd><p>DEPRECATED, use pyarrow.ChunkedArray.to_string</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.get_total_buffer_size"> |
| <span class="sig-name descname"><span class="pre">get_total_buffer_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.get_total_buffer_size" title="Permalink to this definition">#</a></dt> |
| <dd><p>The sum of bytes in each buffer referenced by the chunked array.</p> |
| <p>An array may only reference a portion of a buffer. |
| This method will overestimate in this case and return the |
| byte size of the entire buffer.</p> |
| <p>If a buffer is referenced multiple times then it will |
| only be counted once.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">get_total_buffer_size</span><span class="p">()</span> |
| <span class="go">49</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.index"> |
| <span class="sig-name descname"><span class="pre">index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start</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">end</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_pool</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="#pyarrow.ChunkedArray.index" title="Permalink to this definition">#</a></dt> |
| <dd><p>Find the first index of a value.</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.index.html#pyarrow.compute.index" title="pyarrow.compute.index"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.index()</span></code></a> for full usage.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>value</strong><span class="classifier"><a class="reference internal" href="pyarrow.Scalar.html#pyarrow.Scalar" title="pyarrow.Scalar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Scalar</span></code></a> or object</span></dt><dd><p>The value to look for in the array.</p> |
| </dd> |
| <dt><strong>start</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, optional</span></dt><dd><p>The start index where to look for <cite>value</cite>.</p> |
| </dd> |
| <dt><strong>end</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, optional</span></dt><dd><p>The end index where to look for <cite>value</cite>.</p> |
| </dd> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, optional</span></dt><dd><p>A memory pool for potential memory allocations.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>index</strong><span class="classifier"><a class="reference internal" href="pyarrow.Int64Scalar.html#pyarrow.Int64Scalar" title="pyarrow.Int64Scalar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Int64Scalar</span></code></a></span></dt><dd><p>The index of the value in the array (-1 if not found).</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> |
| <span class="go"><pyarrow.Int64Scalar: 2></span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="go"><pyarrow.Int64Scalar: 3></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.is_nan"> |
| <span class="sig-name descname"><span class="pre">is_nan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.is_nan" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return boolean array indicating the NaN values.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="gp">>>> </span><span class="n">arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">arr</span><span class="o">.</span><span class="n">is_nan</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> false,</span> |
| <span class="go"> true,</span> |
| <span class="go"> false,</span> |
| <span class="go"> false,</span> |
| <span class="go"> null,</span> |
| <span class="go"> false</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.is_null"> |
| <span class="sig-name descname"><span class="pre">is_null</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nan_is_null</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="#pyarrow.ChunkedArray.is_null" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return boolean array indicating the null values.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>nan_is_null</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a> (optional, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a>)</span></dt><dd><p>Whether floating-point NaN values should also be considered null.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>array</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a> <a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">is_null</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> false,</span> |
| <span class="go"> false,</span> |
| <span class="go"> false,</span> |
| <span class="go"> false,</span> |
| <span class="go"> true,</span> |
| <span class="go"> false</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.is_valid"> |
| <span class="sig-name descname"><span class="pre">is_valid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.is_valid" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return boolean array indicating the non-null values.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">is_valid</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> true,</span> |
| <span class="go"> true,</span> |
| <span class="go"> true</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> true,</span> |
| <span class="go"> false,</span> |
| <span class="go"> true</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.iterchunks"> |
| <span class="sig-name descname"><span class="pre">iterchunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.iterchunks" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert to an iterator of ChunkArrays.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">n_legs</span><span class="o">.</span><span class="n">iterchunks</span><span class="p">():</span> |
| <span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">null_count</span><span class="p">)</span> |
| <span class="gp">...</span> |
| <span class="go">0</span> |
| <span class="go">1</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.length"> |
| <span class="sig-name descname"><span class="pre">length</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.length" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return length of a ChunkedArray.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">length</span><span class="p">()</span> |
| <span class="go">6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.nbytes"> |
| <span class="sig-name descname"><span class="pre">nbytes</span></span><a class="headerlink" href="#pyarrow.ChunkedArray.nbytes" title="Permalink to this definition">#</a></dt> |
| <dd><p>Total number of bytes consumed by the elements of the chunked array.</p> |
| <p>In other words, the sum of bytes from all buffer ranges referenced.</p> |
| <p>Unlike <cite>get_total_buffer_size</cite> this method will account for array |
| offsets.</p> |
| <p>If buffers are shared between arrays then the shared |
| portion will only be counted multiple times.</p> |
| <p>The dictionary of dictionary arrays will always be counted in their |
| entirety even if the array only references a portion of the dictionary.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">nbytes</span> |
| <span class="go">49</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.null_count"> |
| <span class="sig-name descname"><span class="pre">null_count</span></span><a class="headerlink" href="#pyarrow.ChunkedArray.null_count" title="Permalink to this definition">#</a></dt> |
| <dd><p>Number of null entries</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">null_count</span> |
| <span class="go">1</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.num_chunks"> |
| <span class="sig-name descname"><span class="pre">num_chunks</span></span><a class="headerlink" href="#pyarrow.ChunkedArray.num_chunks" title="Permalink to this definition">#</a></dt> |
| <dd><p>Number of underlying chunks.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">num_chunks</span> |
| <span class="go">2</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.slice"> |
| <span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</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="#pyarrow.ChunkedArray.slice" title="Permalink to this definition">#</a></dt> |
| <dd><p>Compute zero-copy slice of this ChunkedArray</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>offset</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, default 0</span></dt><dd><p>Offset from start of array to slice</p> |
| </dd> |
| <dt><strong>length</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Length of slice (default is until end of batch starting from |
| offset)</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>sliced</strong><span class="classifier"><a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.sort"> |
| <span class="sig-name descname"><span class="pre">sort</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ascending'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.sort" title="Permalink to this definition">#</a></dt> |
| <dd><p>Sort the ChunkedArray</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, default “ascending”</span></dt><dd><p>Which order to sort values in. |
| Accepted values are “ascending”, “descending”.</p> |
| </dd> |
| <dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, optional</span></dt><dd><p>Additional sorting options. |
| As allowed by <code class="xref py py-class docutils literal notranslate"><span class="pre">SortOptions</span></code></p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>result</strong><span class="classifier"><a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.take"> |
| <span class="sig-name descname"><span class="pre">take</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.take" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select values from the chunked array.</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.take.html#pyarrow.compute.take" title="pyarrow.compute.take"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.take()</span></code></a> for full usage.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>indices</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="pyarrow.array.html#pyarrow.array" title="pyarrow.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">array-like</span></code></a></span></dt><dd><p>The indices in the array whose values will be returned.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>taken</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd><p>An array with the same datatype, containing the taken values.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">take</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">])</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.to_numpy"> |
| <span class="sig-name descname"><span class="pre">to_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_copy_only</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="#pyarrow.ChunkedArray.to_numpy" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return a NumPy copy of this array (experimental).</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>zero_copy_only</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Introduced for signature consistence with pyarrow.Array.to_numpy. |
| This must be False here since NumPy arrays’ buffer must be contiguous.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>array</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span> |
| <span class="go">array([ 2, 2, 4, 4, 5, 100])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.to_pandas"> |
| <span class="sig-name descname"><span class="pre">to_pandas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_pool=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">strings_to_categorical=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">zero_copy_only=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">integer_object_nulls=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">date_as_object=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">timestamp_as_object=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">use_threads=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">deduplicate_objects=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">ignore_metadata=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">safe=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">split_blocks=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">self_destruct=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unicode</span> <span class="pre">maps_as_pydicts=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">types_mapper=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">coerce_temporal_nanoseconds=False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.to_pandas" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert to a pandas-compatible NumPy array or DataFrame, as appropriate</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Arrow MemoryPool to use for allocations. Uses the default memory |
| pool if not passed.</p> |
| </dd> |
| <dt><strong>categories</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, default <code class="xref py py-obj docutils literal notranslate"><span class="pre">empty</span></code></span></dt><dd><p>List of fields that should be returned as pandas.Categorical. Only |
| applies to table-like data structures.</p> |
| </dd> |
| <dt><strong>strings_to_categorical</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Encode string (UTF8) and binary types to pandas.Categorical.</p> |
| </dd> |
| <dt><strong>zero_copy_only</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Raise an ArrowException if this function call would require copying |
| the underlying data.</p> |
| </dd> |
| <dt><strong>integer_object_nulls</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Cast integers with nulls to objects</p> |
| </dd> |
| <dt><strong>date_as_object</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Cast dates to objects. If False, convert to datetime64 dtype with |
| the equivalent time unit (if supported). Note: in pandas version |
| < 2.0, only datetime64[ns] conversion is supported.</p> |
| </dd> |
| <dt><strong>timestamp_as_object</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Cast non-nanosecond timestamps (np.datetime64) to objects. This is |
| useful in pandas version 1.x if you have timestamps that don’t fit |
| in the normal date range of nanosecond timestamps (1678 CE-2262 CE). |
| Non-nanosecond timestamps are supported in pandas version 2.0. |
| If False, all timestamps are converted to datetime64 dtype.</p> |
| </dd> |
| <dt><strong>use_threads</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Whether to parallelize the conversion using multiple threads.</p> |
| </dd> |
| <dt><strong>deduplicate_objects</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Do not create multiple copies Python objects when created, to save |
| on memory use. Conversion will be slower.</p> |
| </dd> |
| <dt><strong>ignore_metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True, do not use the ‘pandas’ metadata to reconstruct the |
| DataFrame index, if present</p> |
| </dd> |
| <dt><strong>safe</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>For certain data types, a cast is needed in order to store the |
| data in a pandas DataFrame or Series (e.g. timestamps are always |
| stored as nanoseconds in pandas). This option controls whether it |
| is a safe cast or not.</p> |
| </dd> |
| <dt><strong>split_blocks</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True, generate one internal “block” for each column when |
| creating a pandas.DataFrame from a RecordBatch or Table. While this |
| can temporarily reduce memory note that various pandas operations |
| can trigger “consolidation” which may balloon memory use.</p> |
| </dd> |
| <dt><strong>self_destruct</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>EXPERIMENTAL: If True, attempt to deallocate the originating Arrow |
| memory while converting the Arrow object to pandas. If you use the |
| object after calling to_pandas with this option it will crash your |
| program.</p> |
| <p>Note that you may not see always memory usage improvements. For |
| example, if multiple columns share an underlying allocation, |
| memory can’t be freed until all columns are converted.</p> |
| </dd> |
| <dt><strong>maps_as_pydicts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, optional, default <cite>None</cite></span></dt><dd><p>Valid values are <cite>None</cite>, ‘lossy’, or ‘strict’. |
| The default behavior (<cite>None</cite>), is to convert Arrow Map arrays to |
| Python association lists (list-of-tuples) in the same order as the |
| Arrow Map, as in [(key1, value1), (key2, value2), …].</p> |
| <p>If ‘lossy’ or ‘strict’, convert Arrow Map arrays to native Python dicts. |
| This can change the ordering of (key, value) pairs, and will |
| deduplicate multiple keys, resulting in a possible loss of data.</p> |
| <p>If ‘lossy’, this key deduplication results in a warning printed |
| when detected. If ‘strict’, this instead results in an exception |
| being raised when detected.</p> |
| </dd> |
| <dt><strong>types_mapper</strong><span class="classifier">function, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>A function mapping a pyarrow DataType to a pandas ExtensionDtype. |
| This can be used to override the default pandas type for conversion |
| of built-in pyarrow types or in absence of pandas_metadata in the |
| Table schema. The function receives a pyarrow DataType and is |
| expected to return a pandas ExtensionDtype or <code class="docutils literal notranslate"><span class="pre">None</span></code> if the |
| default conversion should be used for that type. If you have |
| a dictionary mapping, you can pass <code class="docutils literal notranslate"><span class="pre">dict.get</span></code> as function.</p> |
| </dd> |
| <dt><strong>coerce_temporal_nanoseconds</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Only applicable to pandas version >= 2.0. |
| A legacy option to coerce date32, date64, duration, and timestamp |
| time units to nanoseconds when converting to pandas. This is the |
| default behavior in pandas version 1.x. Set this option to True if |
| you’d like to use this coercion when using pandas version >= 2.0 |
| for backwards compatibility (not recommended otherwise).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.2.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.Series</span></code></a> or <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> depending on <a class="reference internal" href="#pyarrow.ChunkedArray.type" title="pyarrow.ChunkedArray.type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code></a> of object</dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| </pre></div> |
| </div> |
| <p>Convert a Table to pandas DataFrame:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">... </span> <span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 4 Horse</span> |
| <span class="go">2 5 Brittle stars</span> |
| <span class="go">3 100 Centipede</span> |
| <span class="gp">>>> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| <p>Convert a RecordBatch to pandas DataFrame:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 4 Horse</span> |
| <span class="go">2 5 Brittle stars</span> |
| <span class="go">3 100 Centipede</span> |
| <span class="gp">>>> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| <p>Convert a Chunked Array to pandas Series:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go">0 2</span> |
| <span class="go">1 2</span> |
| <span class="go">2 4</span> |
| <span class="go">3 4</span> |
| <span class="go">4 5</span> |
| <span class="go">5 100</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">n_legs</span><span class="o">.</span><span class="n">to_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="go">True</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.to_pylist"> |
| <span class="sig-name descname"><span class="pre">to_pylist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.to_pylist" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert to a list of native Python objects.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">to_pylist</span><span class="p">()</span> |
| <span class="go">[2, 2, 4, 4, None, 100]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.to_string"> |
| <span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">int</span> <span class="pre">indent=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">int</span> <span class="pre">window=5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">int</span> <span class="pre">container_window=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">skip_new_lines=False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.to_string" title="Permalink to this definition">#</a></dt> |
| <dd><p>Render a “pretty-printed” string representation of the ChunkedArray</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>indent</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>How much to indent right the content of the array, |
| by default <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p> |
| </dd> |
| <dt><strong>window</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>How many items to preview within each chunk at the begin and end |
| of the chunk when the chunk is bigger than the window. |
| The other elements will be ellipsed.</p> |
| </dd> |
| <dt><strong>container_window</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>How many chunks to preview at the begin and end |
| of the array when the array is bigger than the window. |
| The other elements will be ellipsed. |
| This setting also applies to list columns.</p> |
| </dd> |
| <dt><strong>skip_new_lines</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If the array should be rendered as a single line of text |
| or if each element should be on its own line.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="n">skip_new_lines</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="go">'[[2,2,4],[4,5,100]]'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.type"> |
| <span class="sig-name descname"><span class="pre">type</span></span><a class="headerlink" href="#pyarrow.ChunkedArray.type" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return data type of a ChunkedArray.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">type</span> |
| <span class="go">DataType(int64)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.unify_dictionaries"> |
| <span class="sig-name descname"><span class="pre">unify_dictionaries</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MemoryPool</span> <span class="pre">memory_pool=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.unify_dictionaries" title="Permalink to this definition">#</a></dt> |
| <dd><p>Unify dictionaries across all chunks.</p> |
| <p>This method returns an equivalent chunked array, but where all |
| chunks share the same dictionary values. Dictionary indices are |
| transposed accordingly.</p> |
| <p>If there are no dictionaries in the chunked array, it is returned |
| unchanged.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>For memory allocations, if required, otherwise use default pool</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>result</strong><span class="classifier"><a class="reference internal" href="#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">arr_1</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">])</span><span class="o">.</span><span class="n">dictionary_encode</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">arr_2</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span><span class="o">.</span><span class="n">dictionary_encode</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">c_arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([</span><span class="n">arr_1</span><span class="p">,</span> <span class="n">arr_2</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">c_arr</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go">...</span> |
| <span class="go"> -- dictionary:</span> |
| <span class="go"> [</span> |
| <span class="go"> "Flamingo",</span> |
| <span class="go"> "Parrot",</span> |
| <span class="go"> "Dog"</span> |
| <span class="go"> ]</span> |
| <span class="go"> -- indices:</span> |
| <span class="go"> [</span> |
| <span class="go"> 0,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2</span> |
| <span class="go"> ],</span> |
| <span class="go">...</span> |
| <span class="go"> -- dictionary:</span> |
| <span class="go"> [</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> "Brittle stars",</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go"> -- indices:</span> |
| <span class="go"> [</span> |
| <span class="go"> 0,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">c_arr</span><span class="o">.</span><span class="n">unify_dictionaries</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go">...</span> |
| <span class="go"> -- dictionary:</span> |
| <span class="go"> [</span> |
| <span class="go"> "Flamingo",</span> |
| <span class="go"> "Parrot",</span> |
| <span class="go"> "Dog",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> "Brittle stars",</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go"> -- indices:</span> |
| <span class="go"> [</span> |
| <span class="go"> 0,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2</span> |
| <span class="go"> ],</span> |
| <span class="go">...</span> |
| <span class="go"> -- dictionary:</span> |
| <span class="go"> [</span> |
| <span class="go"> "Flamingo",</span> |
| <span class="go"> "Parrot",</span> |
| <span class="go"> "Dog",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> "Brittle stars",</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go"> -- indices:</span> |
| <span class="go"> [</span> |
| <span class="go"> 3,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.unique"> |
| <span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.unique" title="Permalink to this definition">#</a></dt> |
| <dd><p>Compute distinct elements in array</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Array</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.Int64Array object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.validate"> |
| <span class="sig-name descname"><span class="pre">validate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</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="#pyarrow.ChunkedArray.validate" title="Permalink to this definition">#</a></dt> |
| <dd><p>Perform validation checks. An exception is raised if validation fails.</p> |
| <p>By default only cheap validation checks are run. Pass <cite>full=True</cite> |
| for thorough validation checks (potentially O(n)).</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>full</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True, run expensive checks, otherwise cheap checks only.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Raises<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArrowInvalid</span></code></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.ChunkedArray.value_counts"> |
| <span class="sig-name descname"><span class="pre">value_counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.ChunkedArray.value_counts" title="Permalink to this definition">#</a></dt> |
| <dd><p>Compute counts of unique elements in array.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">An</span></code> <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a> of <input <a class="reference internal" href="#pyarrow.ChunkedArray.type" title="pyarrow.ChunkedArray.type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code></a> “Values”, <code class="xref py py-obj docutils literal notranslate"><span class="pre">int64_t</span></code> “Counts”> <code class="xref py py-obj docutils literal notranslate"><span class="pre">structs</span></code></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.StructArray object at ...></span> |
| <span class="go">-- is_valid: all not null</span> |
| <span class="go">-- child 0 type: int64</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">-- child 1 type: int64</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 1</span> |
| <span class="go"> ]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| </section> |
| |
| |
| </article> |
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| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.is_null"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.is_null()</span></code></a></li> |
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| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.iterchunks"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.iterchunks()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.length"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.length()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.nbytes"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.nbytes</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.null_count"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.null_count</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.num_chunks"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.num_chunks</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.slice"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.slice()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.sort"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.sort()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.take"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.take()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.to_numpy"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.to_numpy()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.to_pandas"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.to_pandas()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.to_pylist"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.to_pylist()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.to_string"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.to_string()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.type"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.type</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.unify_dictionaries"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.unify_dictionaries()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.unique"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.unique()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.validate"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.validate()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.ChunkedArray.value_counts"><code class="docutils literal notranslate"><span class="pre">ChunkedArray.value_counts()</span></code></a></li> |
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