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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_uint32.html">pyarrow.types.is_uint32</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_uint64.html">pyarrow.types.is_uint64</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_floating.html">pyarrow.types.is_floating</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_float32.html">pyarrow.types.is_float32</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_date64.html">pyarrow.types.is_date64</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_time32.html">pyarrow.types.is_time32</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_time64.html">pyarrow.types.is_time64</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.TypesEnum.html">pyarrow.types.TypesEnum</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/arrays.html">Arrays and Scalars</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.array.html">pyarrow.array</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Int8Array.html">pyarrow.Int8Array</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Int16Array.html">pyarrow.Int16Array</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.DictionaryScalar.html">pyarrow.DictionaryScalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ListScalar.html">pyarrow.ListScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.FixedSizeListScalar.html">pyarrow.FixedSizeListScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.LargeListScalar.html">pyarrow.LargeListScalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.MapScalar.html">pyarrow.MapScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.StructScalar.html">pyarrow.StructScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UnionScalar.html">pyarrow.UnionScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ExtensionScalar.html">pyarrow.ExtensionScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.FixedShapeTensorScalar.html">pyarrow.FixedShapeTensorScalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.JsonScalar.html">pyarrow.JsonScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UuidScalar.html">pyarrow.UuidScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Bool8Scalar.html">pyarrow.Bool8Scalar</a></li> |
| </ul> |
| </details></li> |
| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/memory.html">Buffers and Memory</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.allocate_buffer.html">pyarrow.allocate_buffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.py_buffer.html">pyarrow.py_buffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.foreign_buffer.html">pyarrow.foreign_buffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Buffer.html">pyarrow.Buffer</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.compress.html">pyarrow.compress</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.decompress.html">pyarrow.decompress</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.MemoryPool.html">pyarrow.MemoryPool</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.default_memory_pool.html">pyarrow.default_memory_pool</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.compute.ascii_rpad.html">pyarrow.compute.ascii_rpad</a></li> |
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| </details></li> |
| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/acero.html">Acero - Streaming Execution Engine</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| </ul> |
| </details></li> |
| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/substrait.html">Substrait</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.substrait.deserialize_expressions.html">pyarrow.substrait.deserialize_expressions</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.substrait.serialize_expressions.html">pyarrow.substrait.serialize_expressions</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/flight.html">Arrow Flight</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.initialize_s3.html">pyarrow.fs.initialize_s3</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/dataset.html">Dataset</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <section id="pyarrow-parquet-read-table"> |
| <h1>pyarrow.parquet.read_table<a class="headerlink" href="#pyarrow-parquet-read-table" title="Link to this heading">#</a></h1> |
| <dl class="py function"> |
| <dt class="sig sig-object py" id="pyarrow.parquet.read_table"> |
| <span class="sig-prename descclassname"><span class="pre">pyarrow.parquet.</span></span><span class="sig-name descname"><span class="pre">read_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns</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">use_threads</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">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">use_pandas_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">read_dictionary</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">binary_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">list_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_map</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">buffer_size</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">partitioning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filesystem</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">filters</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">ignore_prefixes</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">pre_buffer</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">coerce_int96_timestamp_unit</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">decryption_properties</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">thrift_string_size_limit</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">thrift_container_size_limit</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">page_checksum_verification</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">arrow_extensions_enabled</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="reference internal" href="../../_modules/pyarrow/parquet/core.html#read_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyarrow.parquet.read_table" title="Link to this definition">#</a></dt> |
| <dd><p>Read a Table from Parquet format</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>source</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><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#list" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference internal" href="pyarrow.NativeFile.html#pyarrow.NativeFile" title="pyarrow.NativeFile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.NativeFile</span></code></a>, or file-like object</span></dt><dd><p>If a string is passed, can be a single file name or directory name. If a |
| list of strings is passed, should be file names. For file-like objects, |
| only read a single file. Use pyarrow.BufferReader to read a file contained |
| in a bytes or buffer-like object.</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 v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>If not None, only these columns will be read from the file. A column |
| name may be a prefix of a nested field, e.g. ‘a’ will select ‘a.b’, |
| ‘a.c’, and ‘a.d.e’. If empty, no columns will be read. Note |
| that the table will still have the correct num_rows set despite having |
| no columns.</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 v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Perform multi-threaded column reads.</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>, optional</span></dt><dd><p>Optionally provide the Schema for the parquet dataset, in which case it |
| will not be inferred from the source.</p> |
| </dd> |
| <dt><strong>use_pandas_metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True and file has custom pandas schema metadata, ensure that |
| index columns are also loaded.</p> |
| </dd> |
| <dt><strong>read_dictionary</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>List of names or column paths (for nested types) to read directly |
| as DictionaryArray. Only supported for BYTE_ARRAY storage. To read |
| a flat column as dictionary-encoded pass the column name. For |
| nested types, you must pass the full column “path”, which could be |
| something like level1.level2.list.item. Refer to the Parquet |
| file’s schema to obtain the paths.</p> |
| </dd> |
| <dt><strong>binary_type</strong><span class="classifier"><a class="reference internal" href="pyarrow.DataType.html#pyarrow.DataType" title="pyarrow.DataType"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.DataType</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If given, Parquet binary columns will be read as this datatype. |
| This setting is ignored if a serialized Arrow schema is found in |
| the Parquet metadata.</p> |
| </dd> |
| <dt><strong>list_type</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">subclass</span></code> of <a class="reference internal" href="pyarrow.DataType.html#pyarrow.DataType" title="pyarrow.DataType"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.DataType</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If given, non-MAP repeated columns will be read as an instance of |
| this datatype (either pyarrow.ListType or pyarrow.LargeListType). |
| This setting is ignored if a serialized Arrow schema is found in |
| the Parquet metadata.</p> |
| </dd> |
| <dt><strong>memory_map</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If the source is a file path, use a memory map to read file, which can |
| improve performance in some environments.</p> |
| </dd> |
| <dt><strong>buffer_size</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, default 0</span></dt><dd><p>If positive, perform read buffering when deserializing individual |
| column chunks. Otherwise IO calls are unbuffered.</p> |
| </dd> |
| <dt><strong>partitioning</strong><span class="classifier"><a class="reference internal" href="pyarrow.dataset.Partitioning.html#pyarrow.dataset.Partitioning" title="pyarrow.dataset.Partitioning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.dataset.Partitioning</span></code></a> or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><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 v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, default “hive”</span></dt><dd><p>The partitioning scheme for a partitioned dataset. The default of “hive” |
| assumes directory names with key=value pairs like “/year=2009/month=11”. |
| In addition, a scheme like “/2009/11” is also supported, in which case |
| you need to specify the field names or a full schema. See the |
| <code class="docutils literal notranslate"><span class="pre">pyarrow.dataset.partitioning()</span></code> function for more details.</p> |
| </dd> |
| <dt><strong>filesystem</strong><span class="classifier"><a class="reference internal" href="pyarrow.fs.FileSystem.html#pyarrow.fs.FileSystem" title="pyarrow.fs.FileSystem"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FileSystem</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If nothing passed, will be inferred based on path. |
| Path will try to be found in the local on-disk filesystem otherwise |
| it will be parsed as an URI to determine the filesystem.</p> |
| </dd> |
| <dt><strong>filters</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.compute.Expression</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">List</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">Tuple</span></code>] or <code class="xref py py-obj docutils literal notranslate"><span class="pre">List</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">List</span></code>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">Tuple</span></code>]], default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Rows which do not match the filter predicate will be removed from scanned |
| data. Partition keys embedded in a nested directory structure will be |
| exploited to avoid loading files at all if they contain no matching rows. |
| Within-file level filtering and different partitioning schemes are supported.</p> |
| <p>Predicates are expressed using an <code class="docutils literal notranslate"><span class="pre">Expression</span></code> or using |
| the disjunctive normal form (DNF), like <code class="docutils literal notranslate"><span class="pre">[[('x',</span> <span class="pre">'=',</span> <span class="pre">0),</span> <span class="pre">...],</span> <span class="pre">...]</span></code>. |
| DNF allows arbitrary boolean logical combinations of single column predicates. |
| The innermost tuples each describe a single column predicate. The list of inner |
| predicates is interpreted as a conjunction (AND), forming a more selective and |
| multiple column predicate. Finally, the most outer list combines these filters |
| as a disjunction (OR).</p> |
| <p>Predicates may also be passed as List[Tuple]. This form is interpreted |
| as a single conjunction. To express OR in predicates, one must |
| use the (preferred) List[List[Tuple]] notation.</p> |
| <p>Each tuple has format: (<code class="docutils literal notranslate"><span class="pre">key</span></code>, <code class="docutils literal notranslate"><span class="pre">op</span></code>, <code class="docutils literal notranslate"><span class="pre">value</span></code>) and compares the |
| <code class="docutils literal notranslate"><span class="pre">key</span></code> with the <code class="docutils literal notranslate"><span class="pre">value</span></code>. |
| The supported <code class="docutils literal notranslate"><span class="pre">op</span></code> are: <code class="docutils literal notranslate"><span class="pre">=</span></code> or <code class="docutils literal notranslate"><span class="pre">==</span></code>, <code class="docutils literal notranslate"><span class="pre">!=</span></code>, <code class="docutils literal notranslate"><span class="pre"><</span></code>, <code class="docutils literal notranslate"><span class="pre">></span></code>, <code class="docutils literal notranslate"><span class="pre"><=</span></code>, |
| <code class="docutils literal notranslate"><span class="pre">>=</span></code>, <code class="docutils literal notranslate"><span class="pre">in</span></code> and <code class="docutils literal notranslate"><span class="pre">not</span> <span class="pre">in</span></code>. If the <code class="docutils literal notranslate"><span class="pre">op</span></code> is <code class="docutils literal notranslate"><span class="pre">in</span></code> or <code class="docutils literal notranslate"><span class="pre">not</span> <span class="pre">in</span></code>, the |
| <code class="docutils literal notranslate"><span class="pre">value</span></code> must be a collection such as a <code class="docutils literal notranslate"><span class="pre">list</span></code>, a <code class="docutils literal notranslate"><span class="pre">set</span></code> or a |
| <code class="docutils literal notranslate"><span class="pre">tuple</span></code>.</p> |
| <p>Examples:</p> |
| <p>Using the <code class="docutils literal notranslate"><span class="pre">Expression</span></code> API:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow.compute</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pc</span> |
| <span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">])</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="s1">'y'</span><span class="p">)</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">})</span> |
| </pre></div> |
| </div> |
| <p>Using the DNF format:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'='</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="p">(</span><span class="s1">'y'</span><span class="p">,</span> <span class="s1">'in'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">])</span> |
| <span class="p">(</span><span class="s1">'z'</span><span class="p">,</span> <span class="s1">'not in'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'a'</span><span class="p">,</span><span class="s1">'b'</span><span class="p">})</span> |
| </pre></div> |
| </div> |
| </dd> |
| <dt><strong>ignore_prefixes</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, optional</span></dt><dd><p>Files matching any of these prefixes will be ignored by the |
| discovery process. |
| This is matched to the basename of a path. |
| By default this is [‘.’, ‘_’]. |
| Note that discovery happens only if a directory is passed as source.</p> |
| </dd> |
| <dt><strong>pre_buffer</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Coalesce and issue file reads in parallel to improve performance on |
| high-latency filesystems (e.g. S3). If True, Arrow will use a |
| background I/O thread pool. If using a filesystem layer that itself |
| performs readahead (e.g. fsspec’s S3FS), disable readahead for best |
| results.</p> |
| </dd> |
| <dt><strong>coerce_int96_timestamp_unit</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Cast timestamps that are stored in INT96 format to a particular |
| resolution (e.g. ‘ms’). Setting to None is equivalent to ‘ns’ |
| and therefore INT96 timestamps will be inferred as timestamps |
| in nanoseconds.</p> |
| </dd> |
| <dt><strong>decryption_properties</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FileDecryptionProperties</span></code> or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>File-level decryption properties. |
| The decryption properties can be created using |
| <code class="docutils literal notranslate"><span class="pre">CryptoFactory.file_decryption_properties()</span></code>.</p> |
| </dd> |
| <dt><strong>thrift_string_size_limit</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If not None, override the maximum total string size allocated |
| when decoding Thrift structures. The default limit should be |
| sufficient for most Parquet files.</p> |
| </dd> |
| <dt><strong>thrift_container_size_limit</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If not None, override the maximum total size of containers allocated |
| when decoding Thrift structures. The default limit should be |
| sufficient for most Parquet files.</p> |
| </dd> |
| <dt><strong>page_checksum_verification</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True, verify the checksum for each page read from the file.</p> |
| </dd> |
| <dt><strong>arrow_extensions_enabled</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><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 v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>If True, read Parquet logical types as Arrow extension types where possible, |
| (e.g., read JSON as the canonical <cite>arrow.json</cite> extension type or UUID as |
| the canonical <cite>arrow.uuid</cite> extension type).</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.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Table</span></code></a></dt><dd><p>Content of the file as a table (of columns)</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Generate an example PyArrow Table and write it to a partitioned dataset:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </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">'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="kn">import</span><span class="w"> </span><span class="nn">pyarrow.parquet</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pq</span> |
| <span class="gp">>>> </span><span class="n">pq</span><span class="o">.</span><span class="n">write_to_dataset</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">root_path</span><span class="o">=</span><span class="s1">'dataset_name_2'</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">partition_cols</span><span class="o">=</span><span class="p">[</span><span class="s1">'year'</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <p>Read the data:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pq</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'dataset_name_2'</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animal year</span> |
| <span class="go">0 5 Brittle stars 2019</span> |
| <span class="go">1 2 Flamingo 2020</span> |
| <span class="go">2 4 Dog 2021</span> |
| <span class="go">3 100 Centipede 2021</span> |
| <span class="go">4 2 Parrot 2022</span> |
| <span class="go">5 4 Horse 2022</span> |
| </pre></div> |
| </div> |
| <p>Read only a subset of columns:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pq</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'dataset_name_2'</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animal"</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[5],[2],[4,100],[2,4]]</span> |
| <span class="go">animal: [["Brittle stars"],["Flamingo"],["Dog","Centipede"],["Parrot","Horse"]]</span> |
| </pre></div> |
| </div> |
| <p>Read a subset of columns and read one column as DictionaryArray:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pq</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'dataset_name_2'</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animal"</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">read_dictionary</span><span class="o">=</span><span class="p">[</span><span class="s2">"animal"</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: dictionary<values=string, indices=int32, ordered=0></span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[5],[2],[4,100],[2,4]]</span> |
| <span class="go">animal: [ -- dictionary:</span> |
| <span class="go">["Brittle stars"] -- indices:</span> |
| <span class="go">[0], -- dictionary:</span> |
| <span class="go">["Flamingo"] -- indices:</span> |
| <span class="go">[0], -- dictionary:</span> |
| <span class="go">["Dog","Centipede"] -- indices:</span> |
| <span class="go">[0,1], -- dictionary:</span> |
| <span class="go">["Parrot","Horse"] -- indices:</span> |
| <span class="go">[0,1]]</span> |
| </pre></div> |
| </div> |
| <p>Read the table with filter:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pq</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'dataset_name_2'</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animal"</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">filters</span><span class="o">=</span><span class="p">[(</span><span class="s1">'n_legs'</span><span class="p">,</span><span class="s1">'<'</span><span class="p">,</span><span class="mi">4</span><span class="p">)])</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animal</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 2 Parrot</span> |
| </pre></div> |
| </div> |
| <p>Read data from a single Parquet file:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pq</span><span class="o">.</span><span class="n">write_table</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="s1">'example.parquet'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pq</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'dataset_name_2'</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animal year</span> |
| <span class="go">0 5 Brittle stars 2019</span> |
| <span class="go">1 2 Flamingo 2020</span> |
| <span class="go">2 4 Dog 2021</span> |
| <span class="go">3 100 Centipede 2021</span> |
| <span class="go">4 2 Parrot 2022</span> |
| <span class="go">5 4 Horse 2022</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| </section> |
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