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| <section id="pyarrow-table"> |
| <h1>pyarrow.Table<a class="headerlink" href="#pyarrow-table" title="Permalink to this heading">#</a></h1> |
| <dl class="py class"> |
| <dt class="sig sig-object py" id="pyarrow.Table"> |
| <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">Table</span></span><a class="headerlink" href="#pyarrow.Table" title="Permalink to this definition">#</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">_Tabular</span></code></p> |
| <p>A collection of top-level named, equal length Arrow arrays.</p> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>Do not call this class’s constructor directly, use one of the <code class="docutils literal notranslate"><span class="pre">from_*</span></code> |
| methods instead.</p> |
| </div> |
| <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">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">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> |
| </pre></div> |
| </div> |
| <p>Construct a Table from arrays:</p> |
| <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">Table</span><span class="o">.</span><span class="n">from_arrays</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="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a RecordBatch:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_batches</span><span class="p">([</span><span class="n">batch</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| <p>Construct a Table from 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">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022,2019,2021]]</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| <span class="go">animals: [["Flamingo","Horse","Brittle stars","Centipede"]]</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pydict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="n">n_legs</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="n">animals</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays with metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">my_metadata</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pylist</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Flamingo'</span><span class="p">},</span> <span class="p">{</span><span class="s1">'year'</span><span class="p">:</span> <span class="mi">2021</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Centipede'</span><span class="p">}]</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,null]]</span> |
| <span class="go">animals: [["Flamingo","Centipede"]]</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows with pyarrow schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'year'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"year"</span><span class="p">:</span> <span class="s2">"Year of entry"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">year: 'Year of entry'</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table with <a class="reference internal" href="pyarrow.table.html#pyarrow.table" title="pyarrow.table"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.table()</span></code></a>:</p> |
| <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">table</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="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.__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.Table.__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.Table.__init__" title="pyarrow.Table.__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.Table.add_column" title="pyarrow.Table.add_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_column</span></code></a>(self, int i, field_, column)</p></td> |
| <td><p>Add column to Table at position.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.append_column" title="pyarrow.Table.append_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">append_column</span></code></a>(self, field_, column)</p></td> |
| <td><p>Append column at end of columns.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.cast" title="pyarrow.Table.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(self, Schema target_schema[, safe, options])</p></td> |
| <td><p>Cast table values to another schema.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.column" title="pyarrow.Table.column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">column</span></code></a>(self, i)</p></td> |
| <td><p>Select single column from Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.combine_chunks" title="pyarrow.Table.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>Make a new table by combining the chunks this table has.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.drop" title="pyarrow.Table.drop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">drop</span></code></a>(self, columns)</p></td> |
| <td><p>Drop one or more columns and return a new table.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.drop_columns" title="pyarrow.Table.drop_columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">drop_columns</span></code></a>(self, columns)</p></td> |
| <td><p>Drop one or more columns and return a new Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.drop_null" title="pyarrow.Table.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 rows that contain missing values from a Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.equals" title="pyarrow.Table.equals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">equals</span></code></a>(self, Table other, ...)</p></td> |
| <td><p>Check if contents of two tables are equal.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.field" title="pyarrow.Table.field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">field</span></code></a>(self, i)</p></td> |
| <td><p>Select a schema field by its column name or numeric index.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.filter" title="pyarrow.Table.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 rows from the table.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.flatten" title="pyarrow.Table.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 Table.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.from_arrays" title="pyarrow.Table.from_arrays"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_arrays</span></code></a>(arrays[, names, schema, metadata])</p></td> |
| <td><p>Construct a Table from Arrow arrays.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.from_batches" title="pyarrow.Table.from_batches"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_batches</span></code></a>(batches, Schema schema=None)</p></td> |
| <td><p>Construct a Table from a sequence or iterator of Arrow RecordBatches.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.from_pandas" title="pyarrow.Table.from_pandas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pandas</span></code></a>(cls, df, Schema schema=None[, ...])</p></td> |
| <td><p>Convert pandas.DataFrame to an Arrow Table.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.from_pydict" title="pyarrow.Table.from_pydict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pydict</span></code></a>(cls, mapping[, schema, metadata])</p></td> |
| <td><p>Construct a Table or RecordBatch from Arrow arrays or columns.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.from_pylist" title="pyarrow.Table.from_pylist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pylist</span></code></a>(cls, mapping[, schema, metadata])</p></td> |
| <td><p>Construct a Table or RecordBatch from list of rows / dictionaries.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.from_struct_array" title="pyarrow.Table.from_struct_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_struct_array</span></code></a>(struct_array)</p></td> |
| <td><p>Construct a Table from a StructArray.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.get_total_buffer_size" title="pyarrow.Table.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 table.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.group_by" title="pyarrow.Table.group_by"><code class="xref py py-obj docutils literal notranslate"><span class="pre">group_by</span></code></a>(self, keys[, use_threads])</p></td> |
| <td><p>Declare a grouping over the columns of the table.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.itercolumns" title="pyarrow.Table.itercolumns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">itercolumns</span></code></a>(self)</p></td> |
| <td><p>Iterator over all columns in their numerical order.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.join" title="pyarrow.Table.join"><code class="xref py py-obj docutils literal notranslate"><span class="pre">join</span></code></a>(self, right_table, keys[, right_keys, ...])</p></td> |
| <td><p>Perform a join between this table and another one.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.join_asof" title="pyarrow.Table.join_asof"><code class="xref py py-obj docutils literal notranslate"><span class="pre">join_asof</span></code></a>(self, right_table, on, by, tolerance)</p></td> |
| <td><p>Perform an asof join between this table and another one.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.remove_column" title="pyarrow.Table.remove_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove_column</span></code></a>(self, int i)</p></td> |
| <td><p>Create new Table with the indicated column removed.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.rename_columns" title="pyarrow.Table.rename_columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rename_columns</span></code></a>(self, names)</p></td> |
| <td><p>Create new table with columns renamed to provided names.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.replace_schema_metadata" title="pyarrow.Table.replace_schema_metadata"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_schema_metadata</span></code></a>(self[, metadata])</p></td> |
| <td><p>Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None), which deletes any existing metadata.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.select" title="pyarrow.Table.select"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select</span></code></a>(self, columns)</p></td> |
| <td><p>Select columns of the Table.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.set_column" title="pyarrow.Table.set_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_column</span></code></a>(self, int i, field_, column)</p></td> |
| <td><p>Replace column in Table at position.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.slice" title="pyarrow.Table.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 Table.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.sort_by" title="pyarrow.Table.sort_by"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sort_by</span></code></a>(self, sorting, **kwargs)</p></td> |
| <td><p>Sort the Table or RecordBatch by one or multiple columns.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.take" title="pyarrow.Table.take"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take</span></code></a>(self, indices)</p></td> |
| <td><p>Select rows from a Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.to_batches" title="pyarrow.Table.to_batches"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_batches</span></code></a>(self[, max_chunksize])</p></td> |
| <td><p>Convert Table to a list of RecordBatch objects.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.to_pandas" title="pyarrow.Table.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-odd"><td><p><a class="reference internal" href="#pyarrow.Table.to_pydict" title="pyarrow.Table.to_pydict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_pydict</span></code></a>(self)</p></td> |
| <td><p>Convert the Table or RecordBatch to a dict or OrderedDict.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.to_pylist" title="pyarrow.Table.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 the Table or RecordBatch to a list of rows / dictionaries.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.to_reader" title="pyarrow.Table.to_reader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_reader</span></code></a>(self[, max_chunksize])</p></td> |
| <td><p>Convert the Table to a RecordBatchReader.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.to_string" title="pyarrow.Table.to_string"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_string</span></code></a>(self, *[, show_metadata, preview_cols])</p></td> |
| <td><p>Return human-readable string representation of Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.to_struct_array" title="pyarrow.Table.to_struct_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_struct_array</span></code></a>(self[, max_chunksize])</p></td> |
| <td><p>Convert to a chunked array of struct type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.unify_dictionaries" title="pyarrow.Table.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.Table.validate" title="pyarrow.Table.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> |
| </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.Table.column_names" title="pyarrow.Table.column_names"><code class="xref py py-obj docutils literal notranslate"><span class="pre">column_names</span></code></a></p></td> |
| <td><p>Names of the Table or RecordBatch columns.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.columns" title="pyarrow.Table.columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">columns</span></code></a></p></td> |
| <td><p>List of all columns in numerical order.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.nbytes" title="pyarrow.Table.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 table.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.num_columns" title="pyarrow.Table.num_columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_columns</span></code></a></p></td> |
| <td><p>Number of columns in this table.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.num_rows" title="pyarrow.Table.num_rows"><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_rows</span></code></a></p></td> |
| <td><p>Number of rows in this table.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.Table.schema" title="pyarrow.Table.schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">schema</span></code></a></p></td> |
| <td><p>Schema of the table and its columns.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.Table.shape" title="pyarrow.Table.shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shape</span></code></a></p></td> |
| <td><p>Dimensions of the table or record batch: (#rows, #columns).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.__dataframe__"> |
| <span class="sig-name descname"><span class="pre">__dataframe__</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">nan_as_null</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.__dataframe__" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return the dataframe interchange object implementing the interchange protocol.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>nan_as_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>, 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 to tell the DataFrame to overwrite null values in the data |
| with <code class="docutils literal notranslate"><span class="pre">NaN</span></code> (or <code class="docutils literal notranslate"><span class="pre">NaT</span></code>).</p> |
| </dd> |
| <dt><strong>allow_copy</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 allow memory copying when exporting. If set to False |
| it would cause non-zero-copy exports to fail.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<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">DataFrame</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">interchange</span></code> object</dt><dd><p>The object which consuming library can use to ingress the dataframe.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Notes</p> |
| <p>Details on the interchange protocol: |
| <a class="reference external" href="https://data-apis.org/dataframe-protocol/latest/index.html">https://data-apis.org/dataframe-protocol/latest/index.html</a> |
| <cite>nan_as_null</cite> currently has no effect; once support for nullable extension |
| dtypes is added, this value should be propagated to columns.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.add_column"> |
| <span class="sig-name descname"><span class="pre">add_column</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">int</span> <span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">field_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.add_column" title="Permalink to this definition">#</a></dt> |
| <dd><p>Add column to Table at position.</p> |
| <p>A new table is returned with the column added, the original table |
| object is left unchanged.</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><p>Index to place the column at.</p> |
| </dd> |
| <dt><strong>field_</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> or <a class="reference internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></span></dt><dd><p>If a string is passed then the type is deduced from the column |
| data.</p> |
| </dd> |
| <dt><strong>column</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>, <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.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a>, or values coercible to arrays</span></dt><dd><p>Column data.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></dt><dd><p>New table with the passed column added.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Add column:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">year</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">add_column</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="s2">"year"</span><span class="p">,</span> <span class="p">[</span><span class="n">year</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2021,2022,2019,2021]]</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| <span class="go">animals: [["Flamingo","Horse","Brittle stars","Centipede"]]</span> |
| </pre></div> |
| </div> |
| <p>Original table is left unchanged:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.append_column"> |
| <span class="sig-name descname"><span class="pre">append_column</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">field_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.append_column" title="Permalink to this definition">#</a></dt> |
| <dd><p>Append column at end of columns.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>field_</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> or <a class="reference internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></span></dt><dd><p>If a string is passed then the type is deduced from the column |
| data.</p> |
| </dd> |
| <dt><strong>column</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 <code class="xref py py-obj docutils literal notranslate"><span class="pre">value</span></code> coercible to <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></span></dt><dd><p>Column data.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>New table or record batch with the passed column added.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Append column at the end:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">year</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">append_column</span><span class="p">(</span><span class="s1">'year'</span><span class="p">,</span> <span class="p">[</span><span class="n">year</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">year: int64</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="go">year: [[2021,2022,2019,2021]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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">Schema</span> <span class="pre">target_schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">safe=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.cast" title="Permalink to this definition">#</a></dt> |
| <dd><p>Cast table values to another schema.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>target_schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</span></code></a></span></dt><dd><p>Schema to cast to, the names and order of fields must match.</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>Check for overflows or other unsafe conversions.</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 class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...</span> |
| </pre></div> |
| </div> |
| <p>Define new schema and cast table values:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</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">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())]</span> |
| <span class="gp">... </span> <span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">target_schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: duration[s]</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> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.column"> |
| <span class="sig-name descname"><span class="pre">column</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.Table.column" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select single column from Table or RecordBatch.</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> or <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></span></dt><dd><p>The index or name of the column to retrieve.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>column</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> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a>) or <a class="reference internal" href="pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a>)</span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Select a column by numeric index:</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">column</span><span class="p">(</span><span class="mi">0</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">]</span> |
| </pre></div> |
| </div> |
| <p>Select a column by its name:</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">column</span><span class="p">(</span><span class="s2">"animals"</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"> "Flamingo",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> "Brittle stars",</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.Table.column_names"> |
| <span class="sig-name descname"><span class="pre">column_names</span></span><a class="headerlink" href="#pyarrow.Table.column_names" title="Permalink to this definition">#</a></dt> |
| <dd><p>Names of the Table or RecordBatch columns.</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/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of <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> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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="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">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">column_names</span> |
| <span class="go">['n_legs', 'animals']</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.Table.columns"> |
| <span class="sig-name descname"><span class="pre">columns</span></span><a class="headerlink" href="#pyarrow.Table.columns" title="Permalink to this definition">#</a></dt> |
| <dd><p>List of all columns in numerical order.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>columns</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.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a>) or <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.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a>)</span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</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="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</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="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">columns</span> |
| <span class="go">[<pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> null,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> null</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"> "Flamingo",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> null,</span> |
| <span class="go"> "Centipede"</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.Table.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.Table.combine_chunks" title="Permalink to this definition">#</a></dt> |
| <dd><p>Make a new table by combining the chunks this table has.</p> |
| <p>All the underlying chunks in the ChunkedArray of each column are |
| concatenated into zero or one chunk.</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 class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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">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="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="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">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">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="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,2,4],[4,5,100]]</span> |
| <span class="go">animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">combine_chunks</span><span class="p">()</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,2,4,4,5,100]]</span> |
| <span class="go">animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.drop"> |
| <span class="sig-name descname"><span class="pre">drop</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">columns</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.drop" title="Permalink to this definition">#</a></dt> |
| <dd><p>Drop one or more columns and return a new table.</p> |
| <p>Alias of Table.drop_columns, but kept for backwards compatibility.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>columns</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> or <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>[<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>]</span></dt><dd><p>Field name(s) referencing existing column(s).</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></dt><dd><p>New table without the column(s).</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.drop_columns"> |
| <span class="sig-name descname"><span class="pre">drop_columns</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">columns</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.drop_columns" title="Permalink to this definition">#</a></dt> |
| <dd><p>Drop one or more columns and return a new Table or RecordBatch.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>columns</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> or <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>[<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>]</span></dt><dd><p>Field name(s) referencing existing column(s).</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A tabular object without the column(s).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-odd">Raises<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#KeyError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KeyError</span></code></a></dt><dd><p>If any of the passed column names do not exist.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Drop one column:</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">drop_columns</span><span class="p">(</span><span class="s2">"animals"</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| </pre></div> |
| </div> |
| <p>Drop one or more columns:</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">drop_columns</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="go">pyarrow.Table</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.Table.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.Table.drop_null" title="Permalink to this definition">#</a></dt> |
| <dd><p>Remove rows that contain missing values from a Table or RecordBatch.</p> |
| <p>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 usage.</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.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A tabular object with the same schema, with rows containing |
| no missing values.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</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="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">drop_null</span><span class="p">()</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: double</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2022,2021]]</span> |
| <span class="go">n_legs: [[4,100]]</span> |
| <span class="go">animals: [["Horse","Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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">Table</span> <span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">check_metadata=False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.equals" title="Permalink to this definition">#</a></dt> |
| <dd><p>Check if contents of two tables 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.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Table</span></code></a></span></dt><dd><p>Table to compare against.</p> |
| </dd> |
| <dt><strong>check_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>Whether schema metadata equality should be checked as well.</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://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></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">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="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">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</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">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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table_0</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([])</span> |
| <span class="gp">>>> </span><span class="n">table_1</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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="n">names</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">table</span><span class="p">)</span> |
| <span class="go">True</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">table_0</span><span class="p">)</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">table_1</span><span class="p">)</span> |
| <span class="go">True</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">table_1</span><span class="p">,</span> <span class="n">check_metadata</span><span class="o">=</span><span class="kc">True</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.Table.field"> |
| <span class="sig-name descname"><span class="pre">field</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.Table.field" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select a schema field by its column name or numeric 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> or <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></span></dt><dd><p>The index or name of the field to retrieve.</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 internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">pyarrow.Field<n_legs: int64></span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">pyarrow.Field<animals: string></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.filter" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select rows from the table.</p> |
| <p>The Table can be filtered based on a mask, which will be passed to |
| <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> to perform the filtering, or it can |
| be filtered through a boolean <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expression</span></code></a></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> or <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Expression</span></code></a></span></dt><dd><p>The boolean mask or the <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expression</span></code></a> to filter the table 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, does nothing if |
| an <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expression</span></code></a> is used.</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.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></span></dt><dd><p>A table of the same schema, with only the rows selected |
| by applied filtering</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Define an expression and select rows:</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.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| <span class="gp">>>> </span><span class="n">expr</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"year"</span><span class="p">)</span> <span class="o"><=</span> <span class="mi">2020</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">expr</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2019]]</span> |
| <span class="go">n_legs: [[2,5]]</span> |
| <span class="go">animals: [["Flamingo","Brittle stars"]]</span> |
| </pre></div> |
| </div> |
| <p>Define a mask and select rows:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="kc">True</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="gp">>>> </span><span class="n">table</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.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022]]</span> |
| <span class="go">n_legs: [[2,4]]</span> |
| <span class="go">animals: [["Flamingo","Horse"]]</span> |
| <span class="gp">>>> </span><span class="n">table</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="s1">'emit_null'</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022,null]]</span> |
| <span class="go">n_legs: [[2,4,null]]</span> |
| <span class="go">animals: [["Flamingo","Horse",null]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.flatten" title="Permalink to this definition">#</a></dt> |
| <dd><p>Flatten this Table.</p> |
| <p>Each column with a struct type is flattened |
| into one column per struct field. Other columns are left 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 class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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">struct</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="s1">'n_legs'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Parrot'</span><span class="p">},</span> |
| <span class="gp">... </span> <span class="p">{</span><span class="s1">'year'</span><span class="p">:</span> <span class="mi">2022</span><span class="p">,</span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">4</span><span class="p">}])</span> |
| <span class="gp">>>> </span><span class="n">month</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">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</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="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">struct</span><span class="p">,</span><span class="n">month</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">"a"</span><span class="p">,</span> <span class="s2">"month"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">a: struct<animals: string, n_legs: int64, year: int64></span> |
| <span class="go"> child 0, animals: string</span> |
| <span class="go"> child 1, n_legs: int64</span> |
| <span class="go"> child 2, year: int64</span> |
| <span class="go">month: int64</span> |
| <span class="go">----</span> |
| <span class="go">a: [</span> |
| <span class="go"> -- is_valid: all not null</span> |
| <span class="go"> -- child 0 type: string</span> |
| <span class="go">["Parrot",null]</span> |
| <span class="go"> -- child 1 type: int64</span> |
| <span class="go">[2,4]</span> |
| <span class="go"> -- child 2 type: int64</span> |
| <span class="go">[null,2022]]</span> |
| <span class="go">month: [[4,6]]</span> |
| </pre></div> |
| </div> |
| <p>Flatten the columns with struct field:</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">flatten</span><span class="p">()</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">a.animals: string</span> |
| <span class="go">a.n_legs: int64</span> |
| <span class="go">a.year: int64</span> |
| <span class="go">month: int64</span> |
| <span class="go">----</span> |
| <span class="go">a.animals: [["Parrot",null]]</span> |
| <span class="go">a.n_legs: [[2,4]]</span> |
| <span class="go">a.year: [[null,2022]]</span> |
| <span class="go">month: [[4,6]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.from_arrays"> |
| <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_arrays</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names</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">schema</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">metadata</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.Table.from_arrays" title="Permalink to this definition">#</a></dt> |
| <dd><p>Construct a Table from Arrow arrays.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>arrays</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.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Array</span></code></a> or <a class="reference internal" href="pyarrow.ChunkedArray.html#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>Equal-length arrays that should form the table.</p> |
| </dd> |
| <dt><strong>names</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 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</span></dt><dd><p>Names for the table columns. If not passed, schema must be passed.</p> |
| </dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</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>Schema for the created table. If not passed, names must be passed.</p> |
| </dd> |
| <dt><strong>metadata</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> or Mapping, 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>Optional metadata for the schema (if inferred).</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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">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">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> |
| </pre></div> |
| </div> |
| <p>Construct a Table from arrays:</p> |
| <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">Table</span><span class="o">.</span><span class="n">from_arrays</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="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| <p>Construct a Table from arrays with metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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="n">names</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="n">my_metadata</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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="n">names</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="n">my_metadata</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from arrays with pyarrow schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"animals"</span><span class="p">:</span> <span class="s2">"Name of the animal species"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">animals: 'Name of the animal species'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.from_batches"> |
| <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_batches</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Schema</span> <span class="pre">schema=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.from_batches" title="Permalink to this definition">#</a></dt> |
| <dd><p>Construct a Table from a sequence or iterator of Arrow RecordBatches.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>batches</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/glossary.html#term-sequence" title="(in Python v3.12)"><span>sequence</span></a> or iterator of <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></span></dt><dd><p>Sequence of RecordBatch to be converted, all schemas must be equal.</p> |
| </dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</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>If not passed, will be inferred from the first RecordBatch.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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">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">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="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="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</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> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a RecordBatch:</p> |
| <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">Table</span><span class="o">.</span><span class="n">from_batches</span><span class="p">([</span><span class="n">batch</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a sequence of RecordBatches:</p> |
| <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">Table</span><span class="o">.</span><span class="n">from_batches</span><span class="p">([</span><span class="n">batch</span><span class="p">,</span> <span class="n">batch</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</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],[2,4,5,100]]</span> |
| <span class="go">animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.from_pandas"> |
| <em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pandas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Schema</span> <span class="pre">schema=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_index=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthreads=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">safe=True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.from_pandas" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert pandas.DataFrame to an Arrow Table.</p> |
| <p>The column types in the resulting Arrow Table are inferred from the |
| dtypes of the pandas.Series in the DataFrame. In the case of non-object |
| Series, the NumPy dtype is translated to its Arrow equivalent. In the |
| case of <cite>object</cite>, we need to guess the datatype by looking at the |
| Python objects in this Series.</p> |
| <p>Be aware that Series of the <cite>object</cite> dtype don’t carry enough |
| information to always lead to a meaningful Arrow type. In the case that |
| we cannot infer a type, e.g. because the DataFrame is of length 0 or |
| the Series only contains None/nan objects, the type is set to |
| null. This behavior can be avoided by constructing an explicit schema |
| and passing it to this function.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>df</strong><span class="classifier"><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></span></dt><dd></dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Schema</span></code></a>, optional</span></dt><dd><p>The expected schema of the Arrow Table. This can be used to |
| indicate the type of columns if we cannot infer it automatically. |
| If passed, the output will have exactly this schema. Columns |
| specified in the schema that are not found in the DataFrame columns |
| or its index will raise an error. Additional columns or index |
| levels in the DataFrame which are not specified in the schema will |
| be ignored.</p> |
| </dd> |
| <dt><strong>preserve_index</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</span></dt><dd><p>Whether to store the index as an additional column in the resulting |
| <code class="docutils literal notranslate"><span class="pre">Table</span></code>. The default of None will store the index as a column, |
| except for RangeIndex which is stored as metadata only. Use |
| <code class="docutils literal notranslate"><span class="pre">preserve_index=True</span></code> to force it to be stored as a column.</p> |
| </dd> |
| <dt><strong>nthreads</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>If greater than 1, convert columns to Arrow in parallel using |
| indicated number of threads. By default, this follows |
| <a class="reference internal" href="pyarrow.cpu_count.html#pyarrow.cpu_count" title="pyarrow.cpu_count"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.cpu_count()</span></code></a> (may use up to system CPU count threads).</p> |
| </dd> |
| <dt><strong>columns</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>, optional</span></dt><dd><p>List of column to be converted. If None, use all columns.</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>Check for overflows or other unsafe conversions.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.from_pydict"> |
| <em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pydict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</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">metadata</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.Table.from_pydict" title="Permalink to this definition">#</a></dt> |
| <dd><p>Construct a Table or RecordBatch from Arrow arrays or columns.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>mapping</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> or Mapping</span></dt><dd><p>A mapping of strings to Arrays or Python lists.</p> |
| </dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</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>If not passed, will be inferred from the Mapping values.</p> |
| </dd> |
| <dt><strong>metadata</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> or Mapping, 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>Optional metadata for the schema (if inferred).</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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">pydict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="n">n_legs</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="n">animals</span><span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays:</p> |
| <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">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays with metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">my_metadata</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays with pyarrow schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.from_pylist"> |
| <em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pylist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</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">metadata</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.Table.from_pylist" title="Permalink to this definition">#</a></dt> |
| <dd><p>Construct a Table or RecordBatch from list of rows / dictionaries.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>mapping</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 dicts of rows</span></dt><dd><p>A mapping of strings to row values.</p> |
| </dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</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>If not passed, will be inferred from the first row of the |
| mapping values.</p> |
| </dd> |
| <dt><strong>metadata</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> or Mapping, 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>Optional metadata for the schema (if inferred).</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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">pylist</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Flamingo'</span><span class="p">},</span> |
| <span class="gp">... </span> <span class="p">{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Dog'</span><span class="p">}]</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows:</p> |
| <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">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</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]]</span> |
| <span class="go">animals: [["Flamingo","Dog"]]</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows with metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">my_metadata</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows with pyarrow schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.from_struct_array"> |
| <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_struct_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">struct_array</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.from_struct_array" title="Permalink to this definition">#</a></dt> |
| <dd><p>Construct a Table from a StructArray.</p> |
| <p>Each field in the StructArray will become a column in the resulting |
| <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>struct_array</strong><span class="classifier"><a class="reference internal" href="pyarrow.StructArray.html#pyarrow.StructArray" title="pyarrow.StructArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StructArray</span></code></a> or <a class="reference internal" href="pyarrow.ChunkedArray.html#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>Array to construct the table from.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Table</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">struct</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="s1">'n_legs'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Parrot'</span><span class="p">},</span> |
| <span class="gp">... </span> <span class="p">{</span><span class="s1">'year'</span><span class="p">:</span> <span class="mi">2022</span><span class="p">,</span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">4</span><span class="p">}])</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_struct_array</span><span class="p">(</span><span class="n">struct</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> animals n_legs year</span> |
| <span class="go">0 Parrot 2 NaN</span> |
| <span class="go">1 None 4 2022.0</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.get_total_buffer_size" title="Permalink to this definition">#</a></dt> |
| <dd><p>The sum of bytes in each buffer referenced by the table.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</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="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</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="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">get_total_buffer_size</span><span class="p">()</span> |
| <span class="go">76</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.group_by"> |
| <span class="sig-name descname"><span class="pre">group_by</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">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.group_by" title="Permalink to this definition">#</a></dt> |
| <dd><p>Declare a grouping over the columns of the table.</p> |
| <p>Resulting grouping can then be used to perform aggregations |
| with a subsequent <code class="docutils literal notranslate"><span class="pre">aggregate()</span></code> method.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>keys</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> or <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>[<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>]</span></dt><dd><p>Name of the columns that should be used as the grouping key.</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 use multithreading or not. When set to True (the |
| default), no stable ordering of the output is guaranteed.</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 internal" href="pyarrow.TableGroupBy.html#pyarrow.TableGroupBy" title="pyarrow.TableGroupBy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TableGroupBy</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.TableGroupBy.html#pyarrow.TableGroupBy.aggregate" title="pyarrow.TableGroupBy.aggregate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TableGroupBy.aggregate</span></code></a></dt><dd></dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</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">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="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="s1">'animal'</span><span class="p">:</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="s2">"Horse"</span><span class="p">,</span> |
| <span class="gp">... </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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">group_by</span><span class="p">(</span><span class="s1">'year'</span><span class="p">)</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs_sum: int64</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022,2021,2019]]</span> |
| <span class="go">n_legs_sum: [[2,6,104,5]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.itercolumns"> |
| <span class="sig-name descname"><span class="pre">itercolumns</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.Table.itercolumns" title="Permalink to this definition">#</a></dt> |
| <dd><p>Iterator over all columns in their numerical order.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Yields<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">Array</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a>) or <a class="reference internal" href="pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a>)</dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</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="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</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="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</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">table</span><span class="o">.</span><span class="n">itercolumns</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">2</span> |
| <span class="go">1</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.join"> |
| <span class="sig-name descname"><span class="pre">join</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">right_table</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right_keys</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">join_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'left</span> <span class="pre">outer'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">left_suffix</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">right_suffix</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">coalesce_keys</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.join" title="Permalink to this definition">#</a></dt> |
| <dd><p>Perform a join between this table and another one.</p> |
| <p>Result of the join will be a new Table, where further |
| operations can be applied.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>right_table</strong><span class="classifier"><a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></span></dt><dd><p>The table to join to the current one, acting as the right table |
| in the join operation.</p> |
| </dd> |
| <dt><strong>keys</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> or <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>[<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>]</span></dt><dd><p>The columns from current table that should be used as keys |
| of the join operation left side.</p> |
| </dd> |
| <dt><strong>right_keys</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> or <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>[<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 <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>The columns from the right_table that should be used as keys |
| on the join operation right side. |
| When <code class="docutils literal notranslate"><span class="pre">None</span></code> use the same key names as the left table.</p> |
| </dd> |
| <dt><strong>join_type</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 “left outer”</span></dt><dd><p>The kind of join that should be performed, one of |
| (“left semi”, “right semi”, “left anti”, “right anti”, |
| “inner”, “left outer”, “right outer”, “full outer”)</p> |
| </dd> |
| <dt><strong>left_suffix</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 <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>Which suffix to add to left column names. This prevents confusion |
| when the columns in left and right tables have colliding names.</p> |
| </dd> |
| <dt><strong>right_suffix</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 <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>Which suffix to add to the right column names. This prevents confusion |
| when the columns in left and right tables have colliding names.</p> |
| </dd> |
| <dt><strong>coalesce_keys</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>If the duplicated keys should be omitted from one of the sides |
| in the join result.</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 use multithreading or not.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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">pandas</span> <span class="k">as</span> <span class="nn">pd</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">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'id'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'id'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animal'</span><span class="p">:</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">t1</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df1</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">t2</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df2</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Left outer join:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">t1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="s1">'id'</span><span class="p">)</span><span class="o">.</span><span class="n">combine_chunks</span><span class="p">()</span><span class="o">.</span><span class="n">sort_by</span><span class="p">(</span><span class="s1">'year'</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">id: int64</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">----</span> |
| <span class="go">id: [[3,1,2]]</span> |
| <span class="go">year: [[2019,2020,2022]]</span> |
| <span class="go">n_legs: [[5,null,null]]</span> |
| <span class="go">animal: [["Brittle stars",null,null]]</span> |
| </pre></div> |
| </div> |
| <p>Full outer join:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">t1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="s1">'id'</span><span class="p">,</span> <span class="n">join_type</span><span class="o">=</span><span class="s2">"full outer"</span><span class="p">)</span><span class="o">.</span><span class="n">combine_chunks</span><span class="p">()</span><span class="o">.</span><span class="n">sort_by</span><span class="p">(</span><span class="s1">'year'</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">id: int64</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">----</span> |
| <span class="go">id: [[3,1,2,4]]</span> |
| <span class="go">year: [[2019,2020,2022,null]]</span> |
| <span class="go">n_legs: [[5,null,null,100]]</span> |
| <span class="go">animal: [["Brittle stars",null,null,"Centipede"]]</span> |
| </pre></div> |
| </div> |
| <p>Right outer join:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">t1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="s1">'id'</span><span class="p">,</span> <span class="n">join_type</span><span class="o">=</span><span class="s2">"right outer"</span><span class="p">)</span><span class="o">.</span><span class="n">combine_chunks</span><span class="p">()</span><span class="o">.</span><span class="n">sort_by</span><span class="p">(</span><span class="s1">'year'</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">id: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2019,null]]</span> |
| <span class="go">id: [[3,4]]</span> |
| <span class="go">n_legs: [[5,100]]</span> |
| <span class="go">animal: [["Brittle stars","Centipede"]]</span> |
| </pre></div> |
| </div> |
| <p>Right anti join</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">t1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="s1">'id'</span><span class="p">,</span> <span class="n">join_type</span><span class="o">=</span><span class="s2">"right anti"</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">id: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">----</span> |
| <span class="go">id: [[4]]</span> |
| <span class="go">n_legs: [[100]]</span> |
| <span class="go">animal: [["Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.join_asof"> |
| <span class="sig-name descname"><span class="pre">join_asof</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">right_table</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">by</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right_on</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">right_by</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.Table.join_asof" title="Permalink to this definition">#</a></dt> |
| <dd><p>Perform an asof join between this table and another one.</p> |
| <p>This is similar to a left-join except that we match on nearest key rather |
| than equal keys. Both tables must be sorted by the key. This type of join |
| is most useful for time series data that are not perfectly aligned.</p> |
| <p>Optionally match on equivalent keys with “by” before searching with “on”.</p> |
| <p>Result of the join will be a new Table, where further |
| operations can be applied.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>right_table</strong><span class="classifier"><a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></span></dt><dd><p>The table to join to the current one, acting as the right table |
| in the join operation.</p> |
| </dd> |
| <dt><strong>on</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></span></dt><dd><p>The column from current table that should be used as the “on” key |
| of the join operation left side.</p> |
| <p>An inexact match is used on the “on” key, i.e. a row is considered a |
| match if and only if left_on - tolerance <= right_on <= left_on.</p> |
| <p>The input dataset must be sorted by the “on” key. Must be a single |
| field of a common type.</p> |
| <p>Currently, the “on” key must be an integer, date, or timestamp type.</p> |
| </dd> |
| <dt><strong>by</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> or <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>[<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>]</span></dt><dd><p>The columns from current table that should be used as the keys |
| of the join operation left side. The join operation is then done |
| only for the matches in these columns.</p> |
| </dd> |
| <dt><strong>tolerance</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>The tolerance for inexact “on” key matching. A right row is considered |
| a match with the left row <code class="docutils literal notranslate"><span class="pre">right.on</span> <span class="pre">-</span> <span class="pre">left.on</span> <span class="pre"><=</span> <span class="pre">tolerance</span></code>. The |
| <code class="docutils literal notranslate"><span class="pre">tolerance</span></code> may be:</p> |
| <ul class="simple"> |
| <li><p>negative, in which case a past-as-of-join occurs;</p></li> |
| <li><p>or positive, in which case a future-as-of-join occurs;</p></li> |
| <li><p>or zero, in which case an exact-as-of-join occurs.</p></li> |
| </ul> |
| <p>The tolerance is interpreted in the same units as the “on” key.</p> |
| </dd> |
| <dt><strong>right_on</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> or <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>[<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 <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>The columns from the right_table that should be used as the on key |
| on the join operation right side. |
| When <code class="docutils literal notranslate"><span class="pre">None</span></code> use the same key name as the left table.</p> |
| </dd> |
| <dt><strong>right_by</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> or <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>[<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 <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>The columns from the right_table that should be used as keys |
| on the join operation right side. |
| When <code class="docutils literal notranslate"><span class="pre">None</span></code> use the same key names as the left table.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.Table.nbytes"> |
| <span class="sig-name descname"><span class="pre">nbytes</span></span><a class="headerlink" href="#pyarrow.Table.nbytes" title="Permalink to this definition">#</a></dt> |
| <dd><p>Total number of bytes consumed by the elements of the table.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</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="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</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="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">nbytes</span> |
| <span class="go">72</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.Table.num_columns"> |
| <span class="sig-name descname"><span class="pre">num_columns</span></span><a class="headerlink" href="#pyarrow.Table.num_columns" title="Permalink to this definition">#</a></dt> |
| <dd><p>Number of columns in this table.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</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="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</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="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">num_columns</span> |
| <span class="go">2</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.Table.num_rows"> |
| <span class="sig-name descname"><span class="pre">num_rows</span></span><a class="headerlink" href="#pyarrow.Table.num_rows" title="Permalink to this definition">#</a></dt> |
| <dd><p>Number of rows in this table.</p> |
| <p>Due to the definition of a table, all columns have the same number of |
| rows.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</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="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</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="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">num_rows</span> |
| <span class="go">4</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.remove_column"> |
| <span class="sig-name descname"><span class="pre">remove_column</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">int</span> <span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.remove_column" title="Permalink to this definition">#</a></dt> |
| <dd><p>Create new Table with the indicated column removed.</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><p>Index of column to remove.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></dt><dd><p>New table without the column.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">remove_column</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.rename_columns"> |
| <span class="sig-name descname"><span class="pre">rename_columns</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">names</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.rename_columns" title="Permalink to this definition">#</a></dt> |
| <dd><p>Create new table with columns renamed to provided names.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>names</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>[<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>] or <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>[<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>, <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>]</span></dt><dd><p>List of new column names or mapping of old column names to new column names.</p> |
| <p>If a mapping of old to new column names is passed, then all columns which are |
| found to match a provided old column name will be renamed to the new column name. |
| If any column names are not found in the mapping, a KeyError will be raised.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| <dt class="field-odd">Raises<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#KeyError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KeyError</span></code></a></dt><dd><p>If any of the column names passed in the names mapping do not exist.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">new_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"n"</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">rename_columns</span><span class="p">(</span><span class="n">new_names</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n: int64</span> |
| <span class="go">name: string</span> |
| <span class="go">----</span> |
| <span class="go">n: [[2,4,5,100]]</span> |
| <span class="go">name: [["Flamingo","Horse","Brittle stars","Centipede"]]</span> |
| <span class="gp">>>> </span><span class="n">new_names</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"n"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">:</span> <span class="s2">"name"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">rename_columns</span><span class="p">(</span><span class="n">new_names</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n: int64</span> |
| <span class="go">name: string</span> |
| <span class="go">----</span> |
| <span class="go">n: [[2,4,5,100]]</span> |
| <span class="go">name: [["Flamingo","Horse","Brittle stars","Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.replace_schema_metadata"> |
| <span class="sig-name descname"><span class="pre">replace_schema_metadata</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">metadata</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.Table.replace_schema_metadata" title="Permalink to this definition">#</a></dt> |
| <dd><p>Create shallow copy of table by replacing schema |
| key-value metadata with the indicated new metadata (which may be None), |
| which deletes any existing metadata.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>metadata</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>, 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></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.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Constructing a Table with pyarrow schema and metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</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="n">df</span><span class="p">,</span> <span class="n">my_schema</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| <span class="go">pandas: ...</span> |
| </pre></div> |
| </div> |
| <p>Create a shallow copy of a Table with deleted schema metadata:</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">replace_schema_metadata</span><span class="p">()</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| </pre></div> |
| </div> |
| <p>Create a shallow copy of a Table with new schema metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"animals"</span><span class="p">:</span> <span class="s2">"Which animal"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">replace_schema_metadata</span><span class="p">(</span><span class="n">metadata</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">animals: 'Which animal'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.Table.schema"> |
| <span class="sig-name descname"><span class="pre">schema</span></span><a class="headerlink" href="#pyarrow.Table.schema" title="Permalink to this definition">#</a></dt> |
| <dd><p>Schema of the table and its columns.</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.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' ...</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.select"> |
| <span class="sig-name descname"><span class="pre">select</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">columns</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.select" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select columns of the Table.</p> |
| <p>Returns a new Table with the specified columns, and metadata |
| preserved.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>columns</strong><span class="classifier">list-like</span></dt><dd><p>The column names or integer indices to select.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">select</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022,2019,2021]]</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">select</span><span class="p">([</span><span class="s2">"year"</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022,2019,2021]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.set_column"> |
| <span class="sig-name descname"><span class="pre">set_column</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">int</span> <span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">field_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.set_column" title="Permalink to this definition">#</a></dt> |
| <dd><p>Replace column in Table at position.</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><p>Index to place the column at.</p> |
| </dd> |
| <dt><strong>field_</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> or <a class="reference internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></span></dt><dd><p>If a string is passed then the type is deduced from the column |
| data.</p> |
| </dd> |
| <dt><strong>column</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>, <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.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a>, or values coercible to arrays</span></dt><dd><p>Column data.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></dt><dd><p>New table with the passed column set.</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Replace a column:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">year</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">set_column</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="s1">'year'</span><span class="p">,</span> <span class="p">[</span><span class="n">year</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">year: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| <span class="go">year: [[2021,2022,2019,2021]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.Table.shape"> |
| <span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#pyarrow.Table.shape" title="Permalink to this definition">#</a></dt> |
| <dd><p>Dimensions of the table or record batch: (#rows, #columns).</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>, <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><p>Number of rows and number of columns.</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">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="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</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="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</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="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">shape</span> |
| <span class="go">(4, 2)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.slice" title="Permalink to this definition">#</a></dt> |
| <dd><p>Compute zero-copy slice of this Table.</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 table 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 table starting from |
| offset).</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="n">length</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022,2019]]</span> |
| <span class="go">n_legs: [[2,4,5]]</span> |
| <span class="go">animals: [["Flamingo","Horse","Brittle stars"]]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="n">offset</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2019,2021]]</span> |
| <span class="go">n_legs: [[5,100]]</span> |
| <span class="go">animals: [["Brittle stars","Centipede"]]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="n">offset</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2019]]</span> |
| <span class="go">n_legs: [[5]]</span> |
| <span class="go">animals: [["Brittle stars"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.sort_by"> |
| <span class="sig-name descname"><span class="pre">sort_by</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">sorting</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.Table.sort_by" title="Permalink to this definition">#</a></dt> |
| <dd><p>Sort the Table or RecordBatch by one or multiple columns.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>sorting</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> or <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>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a>(<code class="xref py py-obj docutils literal notranslate"><span class="pre">name</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">order</span></code>)]</span></dt><dd><p>Name of the column to use to sort (ascending), or |
| a list of multiple sorting conditions where |
| each entry is a tuple with column name |
| and sorting order (“ascending” or “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 class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A new tabular object sorted according to the sort keys.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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">pandas</span> <span class="k">as</span> <span class="nn">pd</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">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="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="s1">'animal'</span><span class="p">:</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="s2">"Horse"</span><span class="p">,</span> |
| <span class="gp">... </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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">sort_by</span><span class="p">(</span><span class="s1">'animal'</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2019,2021,2021,2020,2022,2022]]</span> |
| <span class="go">n_legs: [[5,100,4,2,4,2]]</span> |
| <span class="go">animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.take" title="Permalink to this definition">#</a></dt> |
| <dd><p>Select rows from a Table or RecordBatch.</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 tabular object whose rows will be returned.</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 internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A tabular object with the same schema, containing the taken rows.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</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">3</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2022,2021]]</span> |
| <span class="go">n_legs: [[4,100]]</span> |
| <span class="go">animals: [["Horse","Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.to_batches"> |
| <span class="sig-name descname"><span class="pre">to_batches</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">max_chunksize</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.Table.to_batches" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert Table to a list of RecordBatch objects.</p> |
| <p>Note that this method is zero-copy, it merely exposes the same data |
| under a different API.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>max_chunksize</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>Maximum number of rows for each RecordBatch chunk. Individual chunks |
| may be smaller depending on the chunk layout of individual columns.</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://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>[<a class="reference internal" href="pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</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="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Convert a Table to a RecordBatch:</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">to_batches</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</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> |
| </pre></div> |
| </div> |
| <p>Convert a Table to a list of RecordBatches:</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">to_batches</span><span class="p">(</span><span class="n">max_chunksize</span><span class="o">=</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</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="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_batches</span><span class="p">(</span><span class="n">max_chunksize</span><span class="o">=</span><span class="mi">2</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</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 5 Brittle stars</span> |
| <span class="go">1 100 Centipede</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.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 external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.12)"><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.Table.to_pydict"> |
| <span class="sig-name descname"><span class="pre">to_pydict</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.Table.to_pydict" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert the Table or RecordBatch to a dict or OrderedDict.</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/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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">2</span><span class="p">,</span> <span class="mi">4</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">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</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="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">table</span><span class="o">.</span><span class="n">to_pydict</span><span class="p">()</span> |
| <span class="go">{'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.to_pylist" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert the Table or RecordBatch to a list of rows / dictionaries.</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/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</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">data</span> <span class="o">=</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="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">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="n">data</span><span class="p">,</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">table</span><span class="o">.</span><span class="n">to_pylist</span><span class="p">()</span> |
| <span class="go">[{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.to_reader"> |
| <span class="sig-name descname"><span class="pre">to_reader</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">max_chunksize</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.Table.to_reader" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert the Table to a RecordBatchReader.</p> |
| <p>Note that this method is zero-copy, it merely exposes the same data |
| under a different API.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>max_chunksize</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>Maximum number of rows for each RecordBatch chunk. Individual chunks |
| may be smaller depending on the chunk layout of individual columns.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt>RecordBatchReader</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> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</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="s1">'animals'</span><span class="p">:</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">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Convert a Table to a RecordBatchReader:</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">to_reader</span><span class="p">()</span> |
| <span class="go"><pyarrow.lib.RecordBatchReader object at ...></span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">reader</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">to_reader</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">reader</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...</span> |
| <span class="gp">>>> </span><span class="n">reader</span><span class="o">.</span><span class="n">read_all</span><span class="p">()</span> |
| <span class="go">pyarrow.Table</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> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preview_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.Table.to_string" title="Permalink to this definition">#</a></dt> |
| <dd><p>Return human-readable string representation of Table or RecordBatch.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>show_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>Display Field-level and Schema-level KeyValueMetadata.</p> |
| </dd> |
| <dt><strong>preview_cols</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>Display values of the columns for the first N columns.</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://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.Table.to_struct_array"> |
| <span class="sig-name descname"><span class="pre">to_struct_array</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">max_chunksize</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.Table.to_struct_array" title="Permalink to this definition">#</a></dt> |
| <dd><p>Convert to a chunked array of struct type.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>max_chunksize</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>Maximum number of rows for ChunkedArray chunks. Individual chunks |
| may be smaller depending on the chunk layout of individual columns.</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 internal" href="pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.unify_dictionaries" title="Permalink to this definition">#</a></dt> |
| <dd><p>Unify dictionaries across all chunks.</p> |
| <p>This method returns an equivalent table, but where all chunks of |
| each column share the same dictionary values. Dictionary indices |
| are transposed accordingly.</p> |
| <p>Columns without dictionaries are 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 class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</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">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">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="n">c_arr</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">animals: dictionary<values=string, indices=int32, ordered=0></span> |
| <span class="go">----</span> |
| <span class="go">animals: [ -- dictionary:</span> |
| <span class="go">["Flamingo","Parrot","Dog"] -- indices:</span> |
| <span class="go">[0,1,2], -- dictionary:</span> |
| <span class="go">["Horse","Brittle stars","Centipede"] -- indices:</span> |
| <span class="go">[0,1,2]]</span> |
| </pre></div> |
| </div> |
| <p>Unify dictionaries across both chunks:</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">unify_dictionaries</span><span class="p">()</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">animals: dictionary<values=string, indices=int32, ordered=0></span> |
| <span class="go">----</span> |
| <span class="go">animals: [ -- dictionary:</span> |
| <span class="go">["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices:</span> |
| <span class="go">[0,1,2], -- dictionary:</span> |
| <span class="go">["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices:</span> |
| <span class="go">[3,4,5]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.Table.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.Table.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> |
| |
| </dd></dl> |
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