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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.ExtensionScalar.html">pyarrow.ExtensionScalar</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.Tensor.html">pyarrow.Tensor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2 has-children"><a class="reference internal" href="api/compute.html">Compute Functions</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.negate_checked.html">pyarrow.compute.negate_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sqrt.html">pyarrow.compute.sqrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sqrt_checked.html">pyarrow.compute.sqrt_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.subtract_checked.html">pyarrow.compute.subtract_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_and.html">pyarrow.compute.bit_wise_and</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_not.html">pyarrow.compute.bit_wise_not</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_or.html">pyarrow.compute.bit_wise_or</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.bit_wise_xor.html">pyarrow.compute.bit_wise_xor</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.shift_left_checked.html">pyarrow.compute.shift_left_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.shift_right_checked.html">pyarrow.compute.shift_right_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.ceil.html">pyarrow.compute.ceil</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.floor.html">pyarrow.compute.floor</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.round.html">pyarrow.compute.round</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.round_to_multiple.html">pyarrow.compute.round_to_multiple</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.trunc.html">pyarrow.compute.trunc</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.ln.html">pyarrow.compute.ln</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.ln_checked.html">pyarrow.compute.ln_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log10.html">pyarrow.compute.log10</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log10_checked.html">pyarrow.compute.log10_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log1p.html">pyarrow.compute.log1p</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log1p_checked.html">pyarrow.compute.log1p_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log2.html">pyarrow.compute.log2</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.log2_checked.html">pyarrow.compute.log2_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.logb.html">pyarrow.compute.logb</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.logb_checked.html">pyarrow.compute.logb_checked</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.acos_checked.html">pyarrow.compute.acos_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.asin.html">pyarrow.compute.asin</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.asin_checked.html">pyarrow.compute.asin_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.atan.html">pyarrow.compute.atan</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.atan2.html">pyarrow.compute.atan2</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.cos.html">pyarrow.compute.cos</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.cos_checked.html">pyarrow.compute.cos_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sin.html">pyarrow.compute.sin</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.sin_checked.html">pyarrow.compute.sin_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.tan.html">pyarrow.compute.tan</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.tan_checked.html">pyarrow.compute.tan_checked</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.compute.equal.html">pyarrow.compute.equal</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.ipc.MessageReader.html">pyarrow.ipc.MessageReader</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.flight.FlightUnavailableError.html">pyarrow.flight.FlightUnavailableError</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="generated/pyarrow.flight.FlightWriteSizeExceededError.html">pyarrow.flight.FlightWriteSizeExceededError</a></li> |
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| <section id="extending-pyarrow"> |
| <span id="extending-types"></span><h1>Extending pyarrow<a class="headerlink" href="#extending-pyarrow" title="Permalink to this heading">#</a></h1> |
| <section id="controlling-conversion-to-py-arrow-with-the-pycapsule-interface"> |
| <h2>Controlling conversion to (Py)Arrow with the PyCapsule Interface<a class="headerlink" href="#controlling-conversion-to-py-arrow-with-the-pycapsule-interface" title="Permalink to this heading">#</a></h2> |
| <p>The <a class="reference internal" href="../format/CDataInterface.html#c-data-interface"><span class="std std-ref">Arrow C data interface</span></a> allows moving Arrow data between |
| different implementations of Arrow. This is a generic, cross-language interface not |
| specific to Python, but for Python libraries this interface is extended with a Python |
| specific layer: <a class="reference internal" href="../format/CDataInterface/PyCapsuleInterface.html#arrow-pycapsule-interface"><span class="std std-ref">The Arrow PyCapsule Interface</span></a>.</p> |
| <p>This Python interface ensures that different libraries that support the C Data interface |
| can export Arrow data structures in a standard way and recognize each other’s objects.</p> |
| <p>If you have a Python library providing data structures that hold Arrow-compatible data |
| under the hood, you can implement the following methods on those objects:</p> |
| <ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">__arrow_c_schema__</span></code> for schema or type-like objects.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">__arrow_c_array__</span></code> for arrays and record batches (contiguous tables).</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">__arrow_c_stream__</span></code> for chunked arrays, tables and streams of data.</p></li> |
| </ul> |
| <p>Those methods return <a class="reference external" href="https://docs.python.org/3/c-api/capsule.html">PyCapsule</a> |
| objects, and more details on the exact semantics can be found in the |
| <a class="reference internal" href="../format/CDataInterface/PyCapsuleInterface.html#arrow-pycapsule-interface"><span class="std std-ref">specification</span></a>.</p> |
| <p>When your data structures have those methods defined, the PyArrow constructors |
| (see below) will recognize those objects as |
| supporting this protocol, and convert them to PyArrow data structures zero-copy. And the |
| same can be true for any other library supporting this protocol on ingesting data.</p> |
| <p>Similarly, if your library has functions that accept user-provided data, you can add |
| support for this protocol by checking for the presence of those methods, and |
| therefore accept any Arrow data (instead of harcoding support for a specific |
| Arrow producer such as PyArrow).</p> |
| <p>For consuming data through this protocol with PyArrow, the following constructors |
| can be used to create the various PyArrow objects:</p> |
| <table class="table"> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Result class</p></th> |
| <th class="head"><p>PyArrow constructor</p></th> |
| <th class="head"><p>Supported protocol</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><a class="reference internal" href="generated/pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-class docutils literal notranslate"><span class="pre">Array</span></code></a></p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.array.html#pyarrow.array" title="pyarrow.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.array()</span></code></a></p></td> |
| <td><p>array</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="generated/pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a></p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.chunked_array.html#pyarrow.chunked_array" title="pyarrow.chunked_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.chunked_array()</span></code></a></p></td> |
| <td><p>array, stream</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="generated/pyarrow.RecordBatch.html#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.record_batch.html#pyarrow.record_batch" title="pyarrow.record_batch"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.record_batch()</span></code></a></p></td> |
| <td><p>array</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-class docutils literal notranslate"><span class="pre">Table</span></code></a></p></td> |
| <td><p><a class="reference internal" href="generated/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></td> |
| <td><p>array, stream</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="generated/pyarrow.RecordBatchReader.html#pyarrow.RecordBatchReader" title="pyarrow.RecordBatchReader"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordBatchReader</span></code></a></p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.RecordBatchReader.html#pyarrow.RecordBatchReader.from_stream" title="pyarrow.RecordBatchReader.from_stream"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pyarrow.RecordBatchReader.from_stream()</span></code></a></p></td> |
| <td><p>stream</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="generated/pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-class docutils literal notranslate"><span class="pre">Field</span></code></a></p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.field.html#pyarrow.field" title="pyarrow.field"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.field()</span></code></a></p></td> |
| <td><p>schema</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="generated/pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-class docutils literal notranslate"><span class="pre">Schema</span></code></a></p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.schema.html#pyarrow.schema" title="pyarrow.schema"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.schema()</span></code></a></p></td> |
| <td><p>schema</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>A <a class="reference internal" href="generated/pyarrow.DataType.html#pyarrow.DataType" title="pyarrow.DataType"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataType</span></code></a> can be created by consuming the schema-compatible object |
| using <a class="reference internal" href="generated/pyarrow.field.html#pyarrow.field" title="pyarrow.field"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.field()</span></code></a> and then accessing the <code class="docutils literal notranslate"><span class="pre">.type</span></code> of the resulting |
| Field.</p> |
| </section> |
| <section id="controlling-conversion-to-pyarrow-array-with-the-arrow-array-protocol"> |
| <span id="arrow-array-protocol"></span><h2>Controlling conversion to pyarrow.Array with the <code class="docutils literal notranslate"><span class="pre">__arrow_array__</span></code> protocol<a class="headerlink" href="#controlling-conversion-to-pyarrow-array-with-the-arrow-array-protocol" title="Permalink to this heading">#</a></h2> |
| <p>The <a class="reference internal" href="generated/pyarrow.array.html#pyarrow.array" title="pyarrow.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.array()</span></code></a> function has built-in support for Python sequences, |
| numpy arrays and pandas 1D objects (Series, Index, Categorical, ..) to convert |
| those to Arrow arrays. This can be extended for other array-like objects |
| by implementing the <code class="docutils literal notranslate"><span class="pre">__arrow_array__</span></code> method (similar to numpy’s <code class="docutils literal notranslate"><span class="pre">__array__</span></code> |
| protocol).</p> |
| <p>For example, to support conversion of your duck array class to an Arrow array, |
| define the <code class="docutils literal notranslate"><span class="pre">__arrow_array__</span></code> method to return an Arrow array:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyDuckArray</span><span class="p">:</span> |
| |
| <span class="o">...</span> |
| |
| <span class="k">def</span> <span class="nf">__arrow_array__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="c1"># convert the underlying array values to a pyarrow Array</span> |
| <span class="kn">import</span> <span class="nn">pyarrow</span> |
| <span class="k">return</span> <span class="n">pyarrow</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="o">...</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">type</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>The <code class="docutils literal notranslate"><span class="pre">__arrow_array__</span></code> method takes an optional <cite>type</cite> keyword which is passed |
| through from <a class="reference internal" href="generated/pyarrow.array.html#pyarrow.array" title="pyarrow.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.array()</span></code></a>. The method is allowed to return either |
| a <a class="reference internal" href="generated/pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-class docutils literal notranslate"><span class="pre">Array</span></code></a> or a <a class="reference internal" href="generated/pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a>.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>For a more general way to control the conversion of Python objects to Arrow |
| data consider the <a class="reference internal" href="../format/CDataInterface/PyCapsuleInterface.html"><span class="doc">The Arrow PyCapsule Interface</span></a>. It is |
| not specific to PyArrow and supports converting other objects such as tables |
| and schemas.</p> |
| </div> |
| </section> |
| <section id="defining-extension-types-user-defined-types"> |
| <h2>Defining extension types (“user-defined types”)<a class="headerlink" href="#defining-extension-types-user-defined-types" title="Permalink to this heading">#</a></h2> |
| <p>Arrow has the notion of extension types in the metadata specification as a |
| possibility to extend the built-in types. This is done by annotating any of the |
| built-in Arrow logical types (the “storage type”) with a custom type name and |
| optional serialized representation (“ARROW:extension:name” and |
| “ARROW:extension:metadata” keys in the Field’s custom_metadata of an IPC |
| message). |
| See the <a class="reference internal" href="../format/Columnar.html#format-metadata-extension-types"><span class="std std-ref">Extension Types</span></a> section of the metadata |
| specification for more details.</p> |
| <p>Pyarrow allows you to define such extension types from Python by subclassing |
| <a class="reference internal" href="generated/pyarrow.ExtensionType.html#pyarrow.ExtensionType" title="pyarrow.ExtensionType"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExtensionType</span></code></a> and giving the derived class its own extension name |
| and serialization mechanism. The extension name and serialized metadata |
| can potentially be recognized by other (non-Python) Arrow implementations |
| such as PySpark.</p> |
| <p>For example, we could define a custom UUID type for 128-bit numbers which can |
| be represented as <code class="docutils literal notranslate"><span class="pre">FixedSizeBinary</span></code> type with 16 bytes:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">UuidType</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">ExtensionType</span><span class="p">):</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">binary</span><span class="p">(</span><span class="mi">16</span><span class="p">),</span> <span class="s2">"my_package.uuid"</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__arrow_ext_serialize__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># Since we don't have a parameterized type, we don't need extra</span> |
| <span class="c1"># metadata to be deserialized</span> |
| <span class="k">return</span> <span class="sa">b</span><span class="s1">''</span> |
| |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">__arrow_ext_deserialize__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">storage_type</span><span class="p">,</span> <span class="n">serialized</span><span class="p">):</span> |
| <span class="c1"># Sanity checks, not required but illustrate the method signature.</span> |
| <span class="k">assert</span> <span class="n">storage_type</span> <span class="o">==</span> <span class="n">pa</span><span class="o">.</span><span class="n">binary</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">serialized</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">''</span> |
| <span class="c1"># Return an instance of this subclass given the serialized</span> |
| <span class="c1"># metadata.</span> |
| <span class="k">return</span> <span class="n">UuidType</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <p>The special methods <code class="docutils literal notranslate"><span class="pre">__arrow_ext_serialize__</span></code> and <code class="docutils literal notranslate"><span class="pre">__arrow_ext_deserialize__</span></code> |
| define the serialization of an extension type instance. For non-parametric |
| types such as the above, the serialization payload can be left empty.</p> |
| <p>This can now be used to create arrays and tables holding the extension type:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">uuid_type</span> <span class="o">=</span> <span class="n">UuidType</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">uuid_type</span><span class="o">.</span><span class="n">extension_name</span> |
| <span class="go">'my_package.uuid'</span> |
| <span class="gp">>>> </span><span class="n">uuid_type</span><span class="o">.</span><span class="n">storage_type</span> |
| <span class="go">FixedSizeBinaryType(fixed_size_binary[16])</span> |
| |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">uuid</span> |
| <span class="gp">>>> </span><span class="n">storage_array</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="n">uuid</span><span class="o">.</span><span class="n">uuid4</span><span class="p">()</span><span class="o">.</span><span class="n">bytes</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)],</span> <span class="n">pa</span><span class="o">.</span><span class="n">binary</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">ExtensionArray</span><span class="o">.</span><span class="n">from_storage</span><span class="p">(</span><span class="n">uuid_type</span><span class="p">,</span> <span class="n">storage_array</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">arr</span> |
| <span class="go"><pyarrow.lib.ExtensionArray object at 0x7f75c2f300a0></span> |
| <span class="go">[</span> |
| <span class="go"> A6861959108644B797664AEEE686B682,</span> |
| <span class="go"> 718747F48E5F4058A7261E2B6B228BE8,</span> |
| <span class="go"> 7FE201227D624D96A5CD8639DEF2A68B,</span> |
| <span class="go"> C6CA8C7F95744BFD9462A40B3F57A86C</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <p>This array can be included in RecordBatches, sent over IPC and received in |
| another Python process. The receiving process must explicitly register the |
| extension type for deserialization, otherwise it will fall back to the |
| storage type:</p> |
| <div class="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">register_extension_type</span><span class="p">(</span><span class="n">UuidType</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <p>For example, creating a RecordBatch and writing it to a stream using the |
| IPC protocol:</p> |
| <div class="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">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">arr</span><span class="p">],</span> <span class="p">[</span><span class="s2">"ext"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">sink</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">BufferOutputStream</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatchStreamWriter</span><span class="p">(</span><span class="n">sink</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span> <span class="k">as</span> <span class="n">writer</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">writer</span><span class="o">.</span><span class="n">write_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">buf</span> <span class="o">=</span> <span class="n">sink</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <p>and then reading it back yields the proper type:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">open_stream</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span> <span class="k">as</span> <span class="n">reader</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">result</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read_all</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">result</span><span class="o">.</span><span class="n">column</span><span class="p">(</span><span class="s1">'ext'</span><span class="p">)</span><span class="o">.</span><span class="n">type</span> |
| <span class="go">UuidType(FixedSizeBinaryType(fixed_size_binary[16]))</span> |
| </pre></div> |
| </div> |
| <p>The receiving application doesn’t need to be Python but can still recognize |
| the extension type as a “my_package.uuid” type, if it has implemented its own |
| extension type to receive it. If the type is not registered in the receiving |
| application, it will fall back to the storage type.</p> |
| <section id="parameterized-extension-type"> |
| <h3>Parameterized extension type<a class="headerlink" href="#parameterized-extension-type" title="Permalink to this heading">#</a></h3> |
| <p>The above example used a fixed storage type with no further metadata. But |
| more flexible, parameterized extension types are also possible.</p> |
| <p>The example given here implements an extension type for the <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-span-representation">pandas “period” |
| data type</a>, |
| representing time spans (e.g., a frequency of a day, a month, a quarter, etc). |
| It is stored as an int64 array which is interpreted as the number of time spans |
| of the given frequency since 1970.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">PeriodType</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">ExtensionType</span><span class="p">):</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">freq</span><span class="p">):</span> |
| <span class="c1"># attributes need to be set first before calling</span> |
| <span class="c1"># super init (as that calls serialize)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_freq</span> <span class="o">=</span> <span class="n">freq</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</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="s1">'my_package.period'</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">freq</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_freq</span> |
| |
| <span class="k">def</span> <span class="nf">__arrow_ext_serialize__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="s2">"freq=</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">freq</span><span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">()</span> |
| |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">__arrow_ext_deserialize__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">storage_type</span><span class="p">,</span> <span class="n">serialized</span><span class="p">):</span> |
| <span class="c1"># Return an instance of this subclass given the serialized</span> |
| <span class="c1"># metadata.</span> |
| <span class="n">serialized</span> <span class="o">=</span> <span class="n">serialized</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> |
| <span class="k">assert</span> <span class="n">serialized</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"freq="</span><span class="p">)</span> |
| <span class="n">freq</span> <span class="o">=</span> <span class="n">serialized</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'='</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">PeriodType</span><span class="p">(</span><span class="n">freq</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Here, we ensure to store all information in the serialized metadata that is |
| needed to reconstruct the instance (in the <code class="docutils literal notranslate"><span class="pre">__arrow_ext_deserialize__</span></code> class |
| method), in this case the frequency string.</p> |
| <p>Note that, once created, the data type instance is considered immutable. |
| In the example above, the <code class="docutils literal notranslate"><span class="pre">freq</span></code> parameter is therefore stored in a private |
| attribute with a public read-only property to access it.</p> |
| </section> |
| <section id="custom-extension-array-class"> |
| <h3>Custom extension array class<a class="headerlink" href="#custom-extension-array-class" title="Permalink to this heading">#</a></h3> |
| <p>By default, all arrays with an extension type are constructed or deserialized into |
| a built-in <a class="reference internal" href="generated/pyarrow.ExtensionArray.html#pyarrow.ExtensionArray" title="pyarrow.ExtensionArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExtensionArray</span></code></a> object. Nevertheless, one could want to subclass |
| <a class="reference internal" href="generated/pyarrow.ExtensionArray.html#pyarrow.ExtensionArray" title="pyarrow.ExtensionArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExtensionArray</span></code></a> in order to add some custom logic specific to the extension |
| type. Arrow allows to do so by adding a special method <code class="docutils literal notranslate"><span class="pre">__arrow_ext_class__</span></code> to the |
| definition of the extension type.</p> |
| <p>For instance, let us consider the example from the <a class="reference external" href="https://docs.scipy.org/doc/numpy-1.13.0/user/quickstart.html">Numpy Quickstart</a> of points in 3D space. |
| We can store these as a fixed-size list, where we wish to be able to extract |
| the data as a 2-D Numpy array <code class="docutils literal notranslate"><span class="pre">(N,</span> <span class="pre">3)</span></code> without any copy:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Point3DArray</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">ExtensionArray</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">to_numpy_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">storage</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| |
| |
| <span class="k">class</span> <span class="nc">Point3DType</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">ExtensionType</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">list_</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">float32</span><span class="p">(),</span> <span class="mi">3</span><span class="p">),</span> <span class="s2">"my_package.Point3DType"</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__arrow_ext_serialize__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="sa">b</span><span class="s1">''</span> |
| |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">__arrow_ext_deserialize__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">storage_type</span><span class="p">,</span> <span class="n">serialized</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">Point3DType</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">__arrow_ext_class__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">Point3DArray</span> |
| </pre></div> |
| </div> |
| <p>Arrays built using this extension type now have the expected custom array class:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">storage</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">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="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span> <span class="n">pa</span><span class="o">.</span><span class="n">list_</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">float32</span><span class="p">(),</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">ExtensionArray</span><span class="o">.</span><span class="n">from_storage</span><span class="p">(</span><span class="n">Point3DType</span><span class="p">(),</span> <span class="n">storage</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">arr</span> |
| <span class="go"><__main__.Point3DArray object at 0x7f40dea80670></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 3</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 6</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <p>The additional methods in the extension class are then available to the user:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">arr</span><span class="o">.</span><span class="n">to_numpy_array</span><span class="p">()</span> |
| <span class="go">array([[1., 2., 3.],</span> |
| <span class="go"> [4., 5., 6.]], dtype=float32)</span> |
| </pre></div> |
| </div> |
| <p>This array can be sent over IPC, received in another Python process, and the custom |
| extension array class will be preserved (as long as the receiving process registers |
| the extension type using <a class="reference internal" href="generated/pyarrow.register_extension_type.html#pyarrow.register_extension_type" title="pyarrow.register_extension_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">register_extension_type()</span></code></a> before reading the IPC data).</p> |
| </section> |
| <section id="custom-scalar-conversion"> |
| <h3>Custom scalar conversion<a class="headerlink" href="#custom-scalar-conversion" title="Permalink to this heading">#</a></h3> |
| <p>If you want scalars of your custom extension type to convert to a custom type when |
| <a class="reference internal" href="generated/pyarrow.ExtensionScalar.html#pyarrow.ExtensionScalar.as_py" title="pyarrow.ExtensionScalar.as_py"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ExtensionScalar.as_py()</span></code></a> is called, you can override the |
| <a class="reference internal" href="generated/pyarrow.ExtensionScalar.html#pyarrow.ExtensionScalar.as_py" title="pyarrow.ExtensionScalar.as_py"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ExtensionScalar.as_py()</span></code></a> method by subclassing <a class="reference internal" href="generated/pyarrow.ExtensionScalar.html#pyarrow.ExtensionScalar" title="pyarrow.ExtensionScalar"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExtensionScalar</span></code></a>. |
| For example, if we wanted the above example 3D point type to return a custom |
| 3D point class instead of a list, we would implement:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span> |
| |
| <span class="n">Point3D</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">"Point3D"</span><span class="p">,</span> <span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">,</span> <span class="s2">"z"</span><span class="p">])</span> |
| |
| <span class="k">class</span> <span class="nc">Point3DScalar</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">ExtensionScalar</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">as_py</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Point3D</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">Point3D</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">as_py</span><span class="p">())</span> |
| |
| <span class="k">class</span> <span class="nc">Point3DType</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">ExtensionType</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">list_</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">float32</span><span class="p">(),</span> <span class="mi">3</span><span class="p">),</span> <span class="s2">"my_package.Point3DType"</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__arrow_ext_serialize__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="sa">b</span><span class="s1">''</span> |
| |
| <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">__arrow_ext_deserialize__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">storage_type</span><span class="p">,</span> <span class="n">serialized</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">Point3DType</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">__arrow_ext_scalar_class__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">Point3DScalar</span> |
| </pre></div> |
| </div> |
| <p>Arrays built using this extension type now provide scalars that convert to our <code class="docutils literal notranslate"><span class="pre">Point3D</span></code> class:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">storage</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">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="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span> <span class="n">pa</span><span class="o">.</span><span class="n">list_</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">float32</span><span class="p">(),</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">ExtensionArray</span><span class="o">.</span><span class="n">from_storage</span><span class="p">(</span><span class="n">Point3DType</span><span class="p">(),</span> <span class="n">storage</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">as_py</span><span class="p">()</span> |
| <span class="go">Point3D(x=1.0, y=2.0, z=3.0)</span> |
| |
| <span class="gp">>>> </span><span class="n">arr</span><span class="o">.</span><span class="n">to_pylist</span><span class="p">()</span> |
| <span class="go">[Point3D(x=1.0, y=2.0, z=3.0), Point3D(x=4.0, y=5.0, z=6.0)]</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="conversion-to-pandas"> |
| <h3>Conversion to pandas<a class="headerlink" href="#conversion-to-pandas" title="Permalink to this heading">#</a></h3> |
| <p>The conversion to pandas (in <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table.to_pandas" title="pyarrow.Table.to_pandas"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Table.to_pandas()</span></code></a>) of columns with an |
| extension type can controlled in case there is a corresponding |
| <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extension-types">pandas extension array</a> |
| for your extension type.</p> |
| <p>For this, the <a class="reference internal" href="generated/pyarrow.ExtensionType.html#pyarrow.ExtensionType.to_pandas_dtype" title="pyarrow.ExtensionType.to_pandas_dtype"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ExtensionType.to_pandas_dtype()</span></code></a> method needs to be |
| implemented, and should return a <code class="docutils literal notranslate"><span class="pre">pandas.api.extensions.ExtensionDtype</span></code> |
| subclass instance.</p> |
| <p>Using the pandas period type from above as example, this would look like:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">PeriodType</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">ExtensionType</span><span class="p">):</span> |
| <span class="o">...</span> |
| |
| <span class="k">def</span> <span class="nf">to_pandas_dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">PeriodDtype</span><span class="p">(</span><span class="n">freq</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">freq</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Secondly, the pandas <code class="docutils literal notranslate"><span class="pre">ExtensionDtype</span></code> on its turn needs to have the |
| <code class="docutils literal notranslate"><span class="pre">__from_arrow__</span></code> method implemented: a method that given a pyarrow Array |
| or ChunkedArray of the extension type can construct the corresponding |
| pandas <code class="docutils literal notranslate"><span class="pre">ExtensionArray</span></code>. This method should have the following signature:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyExtensionDtype</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">extensions</span><span class="o">.</span><span class="n">ExtensionDtype</span><span class="p">):</span> |
| <span class="o">...</span> |
| |
| <span class="k">def</span> <span class="nf">__from_arrow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">:</span> <span class="n">pyarrow</span><span class="o">.</span><span class="n">Array</span><span class="o">/</span><span class="n">ChunkedArray</span><span class="p">)</span> <span class="o">-></span> <span class="n">pandas</span><span class="o">.</span><span class="n">ExtensionArray</span><span class="p">:</span> |
| <span class="o">...</span> |
| </pre></div> |
| </div> |
| <p>This way, you can control the conversion of a pyarrow <code class="docutils literal notranslate"><span class="pre">Array</span></code> of your pyarrow |
| extension type to a pandas <code class="docutils literal notranslate"><span class="pre">ExtensionArray</span></code> that can be stored in a DataFrame.</p> |
| </section> |
| <section id="canonical-extension-types"> |
| <h3>Canonical extension types<a class="headerlink" href="#canonical-extension-types" title="Permalink to this heading">#</a></h3> |
| <p>You can find the official list of canonical extension types in the |
| <a class="reference internal" href="../format/CanonicalExtensions.html#format-canonical-extensions"><span class="std std-ref">Canonical Extension Types</span></a> section. Here we add examples on how to |
| use them in pyarrow.</p> |
| <section id="fixed-size-tensor"> |
| <h4>Fixed size tensor<a class="headerlink" href="#fixed-size-tensor" title="Permalink to this heading">#</a></h4> |
| <p>To create an array of tensors with equal shape (fixed shape tensor array) we |
| first need to define a fixed shape tensor extension type with value type |
| and shape:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor_type</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">fixed_shape_tensor</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">int32</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> |
| </pre></div> |
| </div> |
| <p>Then we need the storage array with <a class="reference internal" href="generated/pyarrow.list_.html#pyarrow.list_" title="pyarrow.list_"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.list_()</span></code></a> type where <code class="docutils literal notranslate"><span class="pre">value_type`</span></code> |
| is the fixed shape tensor value type and list size is a product of <code class="docutils literal notranslate"><span class="pre">tensor_type</span></code> |
| shape elements. Then we can create an array of tensors with |
| <code class="docutils literal notranslate"><span class="pre">pa.ExtensionArray.from_storage()</span></code> method:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">arr</span> <span class="o">=</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="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">],</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">]]</span> |
| <span class="gp">>>> </span><span class="n">storage</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="n">arr</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">list_</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">int32</span><span class="p">(),</span> <span class="mi">4</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">tensor_array</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">ExtensionArray</span><span class="o">.</span><span class="n">from_storage</span><span class="p">(</span><span class="n">tensor_type</span><span class="p">,</span> <span class="n">storage</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>We can also create another array of tensors with different value type:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor_type_2</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">fixed_shape_tensor</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">float32</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="gp">>>> </span><span class="n">storage_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="n">arr</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">list_</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">float32</span><span class="p">(),</span> <span class="mi">4</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">tensor_array_2</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">ExtensionArray</span><span class="o">.</span><span class="n">from_storage</span><span class="p">(</span><span class="n">tensor_type_2</span><span class="p">,</span> <span class="n">storage_2</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Extension arrays can be used as columns in <code class="docutils literal notranslate"><span class="pre">pyarrow.Table</span></code> or |
| <code class="docutils literal notranslate"><span class="pre">pyarrow.RecordBatch</span></code>:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</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">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="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">'foo'</span><span class="p">,</span> <span class="s1">'bar'</span><span class="p">,</span> <span class="kc">None</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="kc">True</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">True</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="n">tensor_array</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">tensor_array_2</span> |
| <span class="gp">... </span><span class="p">]</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="s1">'f0'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int8</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s1">'f1'</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="s1">'f2'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">bool_</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s1">'tensors_int'</span><span class="p">,</span> <span class="n">tensor_type</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s1">'tensors_float'</span><span class="p">,</span> <span class="n">tensor_type_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="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">data</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="gp">>>> </span><span class="n">table</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">f0: int8</span> |
| <span class="go">f1: string</span> |
| <span class="go">f2: bool</span> |
| <span class="go">tensors_int: extension<arrow.fixed_shape_tensor[value_type=int32, shape=[2,2]]></span> |
| <span class="go">tensors_float: extension<arrow.fixed_shape_tensor[value_type=float, shape=[2,2]]></span> |
| <span class="go">----</span> |
| <span class="go">f0: [[1,2,3]]</span> |
| <span class="go">f1: [["foo","bar",null]]</span> |
| <span class="go">f2: [[true,null,true]]</span> |
| <span class="go">tensors_int: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]</span> |
| <span class="go">tensors_float: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]</span> |
| </pre></div> |
| </div> |
| <p>We can also convert a tensor array to a single multi-dimensional numpy ndarray. |
| With the conversion the length of the arrow array becomes the first dimension |
| in the numpy ndarray:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">numpy_tensor</span> <span class="o">=</span> <span class="n">tensor_array_2</span><span class="o">.</span><span class="n">to_numpy_ndarray</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">numpy_tensor</span> |
| <span class="go">array([[[ 1., 2.],</span> |
| <span class="go"> [ 3., 4.]],</span> |
| <span class="go"> [[ 10., 20.],</span> |
| <span class="go"> [ 30., 40.]],</span> |
| <span class="go"> [[100., 200.],</span> |
| <span class="go"> [300., 400.]]])</span> |
| <span class="go"> >>> numpy_tensor.shape</span> |
| <span class="go">(3, 2, 2)</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Both optional parameters, <code class="docutils literal notranslate"><span class="pre">permutation</span></code> and <code class="docutils literal notranslate"><span class="pre">dim_names</span></code>, are meant to provide the user |
| with the information about the logical layout of the data compared to the physical layout.</p> |
| <p>The conversion to numpy ndarray is only possible for trivial permutations (<code class="docutils literal notranslate"><span class="pre">None</span></code> or |
| <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">...</span> <span class="pre">N-1]</span></code> where <code class="docutils literal notranslate"><span class="pre">N</span></code> is the number of tensor dimensions).</p> |
| </div> |
| <p>And also the other way around, we can convert a numpy ndarray to a fixed shape tensor array:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">FixedShapeTensorArray</span><span class="o">.</span><span class="n">from_numpy_ndarray</span><span class="p">(</span><span class="n">numpy_tensor</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.FixedShapeTensorArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 3,</span> |
| <span class="go"> 4</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 10,</span> |
| <span class="go"> 20,</span> |
| <span class="go"> 30,</span> |
| <span class="go"> 40</span> |
| <span class="go"> ],</span> |
| <span class="go"> [</span> |
| <span class="go"> 100,</span> |
| <span class="go"> 200,</span> |
| <span class="go"> 300,</span> |
| <span class="go"> 400</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <p>With the conversion the first dimension of the ndarray becomes the length of the pyarrow extension |
| array. We can see in the example that ndarray of shape <code class="docutils literal notranslate"><span class="pre">(3,</span> <span class="pre">2,</span> <span class="pre">2)</span></code> becomes an arrow array of |
| length 3 with tensor elements of shape <code class="docutils literal notranslate"><span class="pre">(2,</span> <span class="pre">2)</span></code>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># ndarray of shape (3, 2, 2)</span> |
| <span class="o">>>></span> <span class="n">numpy_tensor</span><span class="o">.</span><span class="n">shape</span> |
| <span class="p">(</span><span class="mi">3</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="c1"># arrow array of length 3 with tensor elements of shape (2, 2)</span> |
| <span class="o">>>></span> <span class="n">pyarrow_tensor_array</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">FixedShapeTensorArray</span><span class="o">.</span><span class="n">from_numpy_ndarray</span><span class="p">(</span><span class="n">numpy_tensor</span><span class="p">)</span> |
| <span class="o">>>></span> <span class="nb">len</span><span class="p">(</span><span class="n">pyarrow_tensor_array</span><span class="p">)</span> |
| <span class="mi">3</span> |
| <span class="o">>>></span> <span class="n">pyarrow_tensor_array</span><span class="o">.</span><span class="n">type</span><span class="o">.</span><span class="n">shape</span> |
| <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <p>The extension type can also have <code class="docutils literal notranslate"><span class="pre">permutation</span></code> and <code class="docutils literal notranslate"><span class="pre">dim_names</span></code> defined. For |
| example</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor_type</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">fixed_shape_tensor</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">float64</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">3</span><span class="p">],</span> <span class="n">permutation</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <p>or</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor_type</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">fixed_shape_tensor</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">bool_</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">3</span><span class="p">],</span> <span class="n">dim_names</span><span class="o">=</span><span class="p">[</span><span class="s1">'C'</span><span class="p">,</span> <span class="s1">'H'</span><span class="p">,</span> <span class="s1">'W'</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <p>for <code class="docutils literal notranslate"><span class="pre">NCHW</span></code> format where:</p> |
| <ul class="simple"> |
| <li><p>N: number of images which is in our case the length of an array and is always on |
| the first dimension</p></li> |
| <li><p>C: number of channels of the image</p></li> |
| <li><p>H: height of the image</p></li> |
| <li><p>W: width of the image</p></li> |
| </ul> |
| </section> |
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