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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="pandas-integration"> |
| <span id="pandas-interop"></span><h1>Pandas Integration<a class="headerlink" href="#pandas-integration" title="Permalink to this heading">#</a></h1> |
| <p>To interface with <a class="reference external" href="https://pandas.pydata.org/">pandas</a>, PyArrow provides |
| various conversion routines to consume pandas structures and convert back |
| to them.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>While pandas uses NumPy as a backend, it has enough peculiarities |
| (such as a different type system, and support for null values) that this |
| is a separate topic from <a class="reference internal" href="numpy.html#numpy-interop"><span class="std std-ref">NumPy Integration</span></a>.</p> |
| </div> |
| <p>To follow examples in this document, make sure to run:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| |
| <span class="gp">In [2]: </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| </pre></div> |
| </div> |
| <section id="dataframes"> |
| <h2>DataFrames<a class="headerlink" href="#dataframes" title="Permalink to this heading">#</a></h2> |
| <p>The equivalent to a pandas DataFrame in Arrow is a <a class="reference internal" href="data.html#data-table"><span class="std std-ref">Table</span></a>. |
| Both consist of a set of named columns of equal length. While pandas only |
| supports flat columns, the Table also provides nested columns, thus it can |
| represent more data than a DataFrame, so a full conversion is not always possible.</p> |
| <p>Conversion from a Table to a DataFrame is done by calling |
| <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">pyarrow.Table.to_pandas()</span></code></a>. The inverse is then achieved by using |
| <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table.from_pandas" title="pyarrow.Table.from_pandas"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pyarrow.Table.from_pandas()</span></code></a>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> |
| |
| <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]})</span> |
| <span class="c1"># Convert from pandas to Arrow</span> |
| <span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="c1"># Convert back to pandas</span> |
| <span class="n">df_new</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| |
| <span class="c1"># Infer Arrow schema from pandas</span> |
| <span class="n">schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Schema</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>By default <code class="docutils literal notranslate"><span class="pre">pyarrow</span></code> tries to preserve and restore the <code class="docutils literal notranslate"><span class="pre">.index</span></code> |
| data as accurately as possible. See the section below for more about |
| this, and how to disable this logic.</p> |
| </section> |
| <section id="series"> |
| <h2>Series<a class="headerlink" href="#series" title="Permalink to this heading">#</a></h2> |
| <p>In Arrow, the most similar structure to a pandas Series is an Array. |
| It is a vector that contains data of the same type as linear memory. You can |
| convert a pandas Series to an Arrow Array using <a class="reference internal" href="generated/pyarrow.Array.html#pyarrow.Array.from_pandas" title="pyarrow.Array.from_pandas"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pyarrow.Array.from_pandas()</span></code></a>. |
| As Arrow Arrays are always nullable, you can supply an optional mask using |
| the <code class="docutils literal notranslate"><span class="pre">mask</span></code> parameter to mark all null-entries.</p> |
| </section> |
| <section id="handling-pandas-indexes"> |
| <h2>Handling pandas Indexes<a class="headerlink" href="#handling-pandas-indexes" title="Permalink to this heading">#</a></h2> |
| <p>Methods like <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table.from_pandas" title="pyarrow.Table.from_pandas"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pyarrow.Table.from_pandas()</span></code></a> have a |
| <code class="docutils literal notranslate"><span class="pre">preserve_index</span></code> option which defines how to preserve (store) or not |
| to preserve (to not store) the data in the <code class="docutils literal notranslate"><span class="pre">index</span></code> member of the |
| corresponding pandas object. This data is tracked using schema-level |
| metadata in the internal <code class="docutils literal notranslate"><span class="pre">arrow::Schema</span></code> object.</p> |
| <p>The default of <code class="docutils literal notranslate"><span class="pre">preserve_index</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, which behaves as |
| follows:</p> |
| <ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">RangeIndex</span></code> is stored as metadata-only, not requiring any extra |
| storage.</p></li> |
| <li><p>Other index types are stored as one or more physical data columns in |
| the resulting <code class="xref py py-class docutils literal notranslate"><span class="pre">Table</span></code></p></li> |
| </ul> |
| <p>To not store the index at all pass <code class="docutils literal notranslate"><span class="pre">preserve_index=False</span></code>. Since |
| storing a <code class="docutils literal notranslate"><span class="pre">RangeIndex</span></code> can cause issues in some limited scenarios |
| (such as storing multiple DataFrame objects in a Parquet file), to |
| force all index data to be serialized in the resulting table, pass |
| <code class="docutils literal notranslate"><span class="pre">preserve_index=True</span></code>.</p> |
| </section> |
| <section id="type-differences"> |
| <h2>Type differences<a class="headerlink" href="#type-differences" title="Permalink to this heading">#</a></h2> |
| <p>With the current design of pandas and Arrow, it is not possible to convert all |
| column types unmodified. One of the main issues here is that pandas has no |
| support for nullable columns of arbitrary type. Also <code class="docutils literal notranslate"><span class="pre">datetime64</span></code> is currently |
| fixed to nanosecond resolution. On the other side, Arrow might be still missing |
| support for some types.</p> |
| <section id="pandas-arrow-conversion"> |
| <h3>pandas -> Arrow Conversion<a class="headerlink" href="#pandas-arrow-conversion" title="Permalink to this heading">#</a></h3> |
| <table class="table"> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Source Type (pandas)</p></th> |
| <th class="head"><p>Destination Type (Arrow)</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">BOOL</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">(u)int{8,16,32,64}</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">(U)INT{8,16,32,64}</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">float32</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">FLOAT</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">float64</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">DOUBLE</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">str</span></code> / <code class="docutils literal notranslate"><span class="pre">unicode</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">STRING</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">pd.Categorical</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">DICTIONARY</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">pd.Timestamp</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">TIMESTAMP(unit=ns)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">datetime.date</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">DATE</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">datetime.time</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">TIME64</span></code></p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="arrow-pandas-conversion"> |
| <h3>Arrow -> pandas Conversion<a class="headerlink" href="#arrow-pandas-conversion" title="Permalink to this heading">#</a></h3> |
| <table class="table"> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Source Type (Arrow)</p></th> |
| <th class="head"><p>Destination Type (pandas)</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">BOOL</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">BOOL</span></code> <em>with nulls</em></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">object</span></code> (with values <code class="docutils literal notranslate"><span class="pre">True</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code>, <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">(U)INT{8,16,32,64}</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">(u)int{8,16,32,64}</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">(U)INT{8,16,32,64}</span></code> <em>with nulls</em></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">float64</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">FLOAT</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">float32</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">DOUBLE</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">float64</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">STRING</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">str</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">DICTIONARY</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">pd.Categorical</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">TIMESTAMP(unit=*)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">pd.Timestamp</span></code> (<code class="docutils literal notranslate"><span class="pre">np.datetime64[ns]</span></code>)</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">DATE</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">object</span></code> (with <code class="docutils literal notranslate"><span class="pre">datetime.date</span></code> objects)</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">TIME64</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">object</span></code> (with <code class="docutils literal notranslate"><span class="pre">datetime.time</span></code> objects)</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="categorical-types"> |
| <h3>Categorical types<a class="headerlink" href="#categorical-types" title="Permalink to this heading">#</a></h3> |
| <p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html">Pandas categorical</a> |
| columns are converted to <a class="reference internal" href="data.html#data-dictionary"><span class="std std-ref">Arrow dictionary arrays</span></a>, |
| a special array type optimized to handle repeated and limited |
| number of possible values.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [3]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"cat"</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">,</span> <span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">])})</span> |
| |
| <span class="gp">In [4]: </span><span class="n">df</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">categories</span> |
| <span class="gh">Out[4]: </span><span class="go">Index(['a', 'b', 'c'], dtype='object')</span> |
| |
| <span class="gp">In [5]: </span><span class="n">df</span> |
| <span class="gh">Out[5]: </span> |
| <span class="go"> cat</span> |
| <span class="go">0 a</span> |
| <span class="go">1 b</span> |
| <span class="go">2 c</span> |
| <span class="go">3 a</span> |
| <span class="go">4 b</span> |
| <span class="go">5 c</span> |
| |
| <span class="gp">In [6]: </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| |
| <span class="gp">In [7]: </span><span class="n">table</span> |
| <span class="gh">Out[7]: </span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">cat: dictionary<values=string, indices=int8, ordered=0></span> |
| <span class="gt">----</span> |
| <span class="ne">cat</span>: [ -- dictionary: |
| <span class="p">[</span><span class="s2">"a"</span><span class="p">,</span><span class="s2">"b"</span><span class="p">,</span><span class="s2">"c"</span><span class="p">]</span> <span class="o">--</span> <span class="n">indices</span><span class="p">:</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>We can inspect the <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> of the created table and see the |
| same categories of the Pandas DataFrame.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [8]: </span><span class="n">column</span> <span class="o">=</span> <span class="n">table</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="gp">In [9]: </span><span class="n">chunk</span> <span class="o">=</span> <span class="n">column</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| |
| <span class="gp">In [10]: </span><span class="n">chunk</span><span class="o">.</span><span class="n">dictionary</span> |
| <span class="gh">Out[10]: </span> |
| <span class="go"><pyarrow.lib.StringArray object at 0x7fa2c2bb2b60></span> |
| <span class="go">[</span> |
| <span class="go"> "a",</span> |
| <span class="go"> "b",</span> |
| <span class="go"> "c"</span> |
| <span class="go">]</span> |
| |
| <span class="gp">In [11]: </span><span class="n">chunk</span><span class="o">.</span><span class="n">indices</span> |
| <span class="gh">Out[11]: </span> |
| <span class="go"><pyarrow.lib.Int8Array object at 0x7fa2c2bb2920></span> |
| <span class="go">[</span> |
| <span class="go"> 0,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 0,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="datetime-timestamp-types"> |
| <h3>Datetime (Timestamp) types<a class="headerlink" href="#datetime-timestamp-types" title="Permalink to this heading">#</a></h3> |
| <p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html">Pandas Timestamps</a> |
| use the <code class="docutils literal notranslate"><span class="pre">datetime64[ns]</span></code> type in Pandas and are converted to an Arrow |
| <a class="reference internal" href="generated/pyarrow.TimestampArray.html#pyarrow.TimestampArray" title="pyarrow.TimestampArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimestampArray</span></code></a>.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [12]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"datetime"</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s2">"2020-01-01T00:00:00Z"</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s2">"h"</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">3</span><span class="p">)})</span> |
| |
| <span class="gp">In [13]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="gh">Out[13]: </span> |
| <span class="go">datetime datetime64[ns, UTC]</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="gp">In [14]: </span><span class="n">df</span> |
| <span class="gh">Out[14]: </span> |
| <span class="go"> datetime</span> |
| <span class="go">0 2020-01-01 00:00:00+00:00</span> |
| <span class="go">1 2020-01-01 01:00:00+00:00</span> |
| <span class="go">2 2020-01-01 02:00:00+00:00</span> |
| |
| <span class="gp">In [15]: </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| |
| <span class="gp">In [16]: </span><span class="n">table</span> |
| <span class="gh">Out[16]: </span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">datetime: timestamp[ns, tz=UTC]</span> |
| <span class="gt">----</span> |
| <span class="ne">datetime</span>: [[2020-01-01 00:00:00.000000000Z,2020-01-01 01:00:00.000000000Z,2020-01-01 02:00:00.000000000Z]] |
| </pre></div> |
| </div> |
| <p>In this example the Pandas Timestamp is time zone aware |
| (<code class="docutils literal notranslate"><span class="pre">UTC</span></code> on this case), and this information is used to create the Arrow |
| <a class="reference internal" href="generated/pyarrow.TimestampArray.html#pyarrow.TimestampArray" title="pyarrow.TimestampArray"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimestampArray</span></code></a>.</p> |
| </section> |
| <section id="date-types"> |
| <h3>Date types<a class="headerlink" href="#date-types" title="Permalink to this heading">#</a></h3> |
| <p>While dates can be handled using the <code class="docutils literal notranslate"><span class="pre">datetime64[ns]</span></code> type in |
| pandas, some systems work with object arrays of Python’s built-in |
| <code class="docutils literal notranslate"><span class="pre">datetime.date</span></code> object:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [17]: </span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">date</span> |
| |
| <span class="gp">In [18]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">date</span><span class="p">(</span><span class="mi">2018</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">31</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)])</span> |
| |
| <span class="gp">In [19]: </span><span class="n">s</span> |
| <span class="gh">Out[19]: </span> |
| <span class="go">0 2018-12-31</span> |
| <span class="go">1 None</span> |
| <span class="go">2 2000-01-01</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| <p>When converting to an Arrow array, the <code class="docutils literal notranslate"><span class="pre">date32</span></code> type will be used by |
| default:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [20]: </span><span class="n">arr</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">s</span><span class="p">)</span> |
| |
| <span class="gp">In [21]: </span><span class="n">arr</span><span class="o">.</span><span class="n">type</span> |
| <span class="gh">Out[21]: </span><span class="go">DataType(date32[day])</span> |
| |
| <span class="gp">In [22]: </span><span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="gh">Out[22]: </span><span class="go"><pyarrow.Date32Scalar: datetime.date(2018, 12, 31)></span> |
| </pre></div> |
| </div> |
| <p>To use the 64-bit <code class="docutils literal notranslate"><span class="pre">date64</span></code>, specify this explicitly:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [23]: </span><span class="n">arr</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">s</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">'date64'</span><span class="p">)</span> |
| |
| <span class="gp">In [24]: </span><span class="n">arr</span><span class="o">.</span><span class="n">type</span> |
| <span class="gh">Out[24]: </span><span class="go">DataType(date64[ms])</span> |
| </pre></div> |
| </div> |
| <p>When converting back with <code class="docutils literal notranslate"><span class="pre">to_pandas</span></code>, object arrays of |
| <code class="docutils literal notranslate"><span class="pre">datetime.date</span></code> objects are returned:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [25]: </span><span class="n">arr</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="gh">Out[25]: </span> |
| <span class="go">0 2018-12-31</span> |
| <span class="go">1 None</span> |
| <span class="go">2 2000-01-01</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| <p>If you want to use NumPy’s <code class="docutils literal notranslate"><span class="pre">datetime64</span></code> dtype instead, pass |
| <code class="docutils literal notranslate"><span class="pre">date_as_object=False</span></code>:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [26]: </span><span class="n">s2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(</span><span class="n">date_as_object</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> |
| |
| <span class="gp">In [27]: </span><span class="n">s2</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="gh">Out[27]: </span><span class="go">dtype('<M8[ms]')</span> |
| </pre></div> |
| </div> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>As of Arrow <code class="docutils literal notranslate"><span class="pre">0.13</span></code> the parameter <code class="docutils literal notranslate"><span class="pre">date_as_object</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code> |
| by default. Older versions must pass <code class="docutils literal notranslate"><span class="pre">date_as_object=True</span></code> to |
| obtain this behavior</p> |
| </div> |
| </section> |
| <section id="time-types"> |
| <h3>Time types<a class="headerlink" href="#time-types" title="Permalink to this heading">#</a></h3> |
| <p>The builtin <code class="docutils literal notranslate"><span class="pre">datetime.time</span></code> objects inside Pandas data structures will be |
| converted to an Arrow <code class="docutils literal notranslate"><span class="pre">time64</span></code> and <a class="reference internal" href="generated/pyarrow.Time64Array.html#pyarrow.Time64Array" title="pyarrow.Time64Array"><code class="xref py py-class docutils literal notranslate"><span class="pre">Time64Array</span></code></a> respectively.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [28]: </span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">time</span> |
| |
| <span class="gp">In [29]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">time</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">time</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">2</span><span class="p">)])</span> |
| |
| <span class="gp">In [30]: </span><span class="n">s</span> |
| <span class="gh">Out[30]: </span> |
| <span class="go">0 01:01:01</span> |
| <span class="go">1 02:02:02</span> |
| <span class="go">dtype: object</span> |
| |
| <span class="gp">In [31]: </span><span class="n">arr</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">s</span><span class="p">)</span> |
| |
| <span class="gp">In [32]: </span><span class="n">arr</span><span class="o">.</span><span class="n">type</span> |
| <span class="gh">Out[32]: </span><span class="go">Time64Type(time64[us])</span> |
| |
| <span class="gp">In [33]: </span><span class="n">arr</span> |
| <span class="gh">Out[33]: </span> |
| <span class="go"><pyarrow.lib.Time64Array object at 0x7fa2ca754760></span> |
| <span class="go">[</span> |
| <span class="go"> 01:01:01.000000,</span> |
| <span class="go"> 02:02:02.000000</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <p>When converting to pandas, arrays of <code class="docutils literal notranslate"><span class="pre">datetime.time</span></code> objects are returned:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [34]: </span><span class="n">arr</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="gh">Out[34]: </span> |
| <span class="go">0 01:01:01</span> |
| <span class="go">1 02:02:02</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| </section> |
| </section> |
| <section id="nullable-types"> |
| <h2>Nullable types<a class="headerlink" href="#nullable-types" title="Permalink to this heading">#</a></h2> |
| <p>In Arrow all data types are nullable, meaning they support storing missing |
| values. In pandas, however, not all data types have support for missing data. |
| Most notably, the default integer data types do not, and will get casted |
| to float when missing values are introduced. Therefore, when an Arrow array |
| or table gets converted to pandas, integer columns will become float when |
| missing values are present:</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="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="kc">None</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">arr</span> |
| <span class="go"><pyarrow.lib.Int64Array object at 0x7f07d467c640></span> |
| <span class="go">[</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> null</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">arr</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go">0 1.0</span> |
| <span class="go">1 2.0</span> |
| <span class="go">2 NaN</span> |
| <span class="go">dtype: float64</span> |
| </pre></div> |
| </div> |
| <p>Pandas has experimental nullable data types |
| (<a class="reference external" href="https://pandas.pydata.org/docs/user_guide/integer_na.html">https://pandas.pydata.org/docs/user_guide/integer_na.html</a>). Arrows supports |
| round trip conversion for those:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'a'</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"Int64"</span><span class="p">)})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> a</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 <NA></span> |
| |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span> |
| <span class="go">Out[32]:</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">a: int64</span> |
| <span class="go">----</span> |
| <span class="go">a: [[1,2,null]]</span> |
| |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> a</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 <NA></span> |
| |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">a Int64</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| <p>This roundtrip conversion works because metadata about the original pandas |
| DataFrame gets stored in the Arrow table. However, if you have Arrow data (or |
| e.g. a Parquet file) not originating from a pandas DataFrame with nullable |
| data types, the default conversion to pandas will not use those nullable |
| dtypes.</p> |
| <p>The <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">pyarrow.Table.to_pandas()</span></code></a> method has a <code class="docutils literal notranslate"><span class="pre">types_mapper</span></code> keyword |
| that can be used to override the default data type used for the resulting |
| pandas DataFrame. This way, you can instruct Arrow to create a pandas |
| DataFrame using nullable dtypes.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> a</span> |
| <span class="go">0 1.0</span> |
| <span class="go">1 2.0</span> |
| <span class="go">2 NaN</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(</span><span class="n">types_mapper</span><span class="o">=</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="n">pd</span><span class="o">.</span><span class="n">Int64Dtype</span><span class="p">()}</span><span class="o">.</span><span class="n">get</span><span class="p">)</span> |
| <span class="go"> a</span> |
| <span class="go">0 1</span> |
| <span class="go">1 2</span> |
| <span class="go">2 <NA></span> |
| </pre></div> |
| </div> |
| <p>The <code class="docutils literal notranslate"><span class="pre">types_mapper</span></code> keyword expects a function that will return the pandas |
| data type to use given a pyarrow data type. By using the <code class="docutils literal notranslate"><span class="pre">dict.get</span></code> method, |
| we can create such a function using a dictionary.</p> |
| <p>If you want to use all currently supported nullable dtypes by pandas, this |
| dictionary becomes:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">dtype_mapping</span> <span class="o">=</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="n">pd</span><span class="o">.</span><span class="n">Int8Dtype</span><span class="p">(),</span> |
| <span class="n">pa</span><span class="o">.</span><span class="n">int16</span><span class="p">():</span> <span class="n">pd</span><span class="o">.</span><span class="n">Int16Dtype</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="n">pd</span><span class="o">.</span><span class="n">Int32Dtype</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="n">pd</span><span class="o">.</span><span class="n">Int64Dtype</span><span class="p">(),</span> |
| <span class="n">pa</span><span class="o">.</span><span class="n">uint8</span><span class="p">():</span> <span class="n">pd</span><span class="o">.</span><span class="n">UInt8Dtype</span><span class="p">(),</span> |
| <span class="n">pa</span><span class="o">.</span><span class="n">uint16</span><span class="p">():</span> <span class="n">pd</span><span class="o">.</span><span class="n">UInt16Dtype</span><span class="p">(),</span> |
| <span class="n">pa</span><span class="o">.</span><span class="n">uint32</span><span class="p">():</span> <span class="n">pd</span><span class="o">.</span><span class="n">UInt32Dtype</span><span class="p">(),</span> |
| <span class="n">pa</span><span class="o">.</span><span class="n">uint64</span><span class="p">():</span> <span class="n">pd</span><span class="o">.</span><span class="n">UInt64Dtype</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="n">pd</span><span class="o">.</span><span class="n">BooleanDtype</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="n">pd</span><span class="o">.</span><span class="n">Float32Dtype</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="n">pd</span><span class="o">.</span><span class="n">Float64Dtype</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="n">pd</span><span class="o">.</span><span class="n">StringDtype</span><span class="p">(),</span> |
| <span class="p">}</span> |
| |
| <span class="n">df</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(</span><span class="n">types_mapper</span><span class="o">=</span><span class="n">dtype_mapping</span><span class="o">.</span><span class="n">get</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>When using the pandas API for reading Parquet files (<code class="docutils literal notranslate"><span class="pre">pd.read_parquet(..)</span></code>), |
| this can also be achieved by passing <code class="docutils literal notranslate"><span class="pre">use_nullable_dtypes</span></code>:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">use_nullable_dtypes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="memory-usage-and-zero-copy"> |
| <h2>Memory Usage and Zero Copy<a class="headerlink" href="#memory-usage-and-zero-copy" title="Permalink to this heading">#</a></h2> |
| <p>When converting from Arrow data structures to pandas objects using various |
| <code class="docutils literal notranslate"><span class="pre">to_pandas</span></code> methods, one must occasionally be mindful of issues related to |
| performance and memory usage.</p> |
| <p>Since pandas’s internal data representation is generally different from the |
| Arrow columnar format, zero copy conversions (where no memory allocation or |
| computation is required) are only possible in certain limited cases.</p> |
| <p>In the worst case scenario, calling <code class="docutils literal notranslate"><span class="pre">to_pandas</span></code> will result in two versions |
| of the data in memory, one for Arrow and one for pandas, yielding approximately |
| twice the memory footprint. We have implement some mitigations for this case, |
| particularly when creating large <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects, that we describe below.</p> |
| <section id="zero-copy-series-conversions"> |
| <h3>Zero Copy Series Conversions<a class="headerlink" href="#zero-copy-series-conversions" title="Permalink to this heading">#</a></h3> |
| <p>Zero copy conversions from <code class="docutils literal notranslate"><span class="pre">Array</span></code> or <code class="docutils literal notranslate"><span class="pre">ChunkedArray</span></code> to NumPy arrays or |
| pandas Series are possible in certain narrow cases:</p> |
| <ul class="simple"> |
| <li><p>The Arrow data is stored in an integer (signed or unsigned <code class="docutils literal notranslate"><span class="pre">int8</span></code> through |
| <code class="docutils literal notranslate"><span class="pre">int64</span></code>) or floating point type (<code class="docutils literal notranslate"><span class="pre">float16</span></code> through <code class="docutils literal notranslate"><span class="pre">float64</span></code>). This |
| includes many numeric types as well as timestamps.</p></li> |
| <li><p>The Arrow data has no null values (since these are represented using bitmaps |
| which are not supported by pandas).</p></li> |
| <li><p>For <code class="docutils literal notranslate"><span class="pre">ChunkedArray</span></code>, the data consists of a single chunk, |
| i.e. <code class="docutils literal notranslate"><span class="pre">arr.num_chunks</span> <span class="pre">==</span> <span class="pre">1</span></code>. Multiple chunks will always require a copy |
| because of pandas’s contiguousness requirement.</p></li> |
| </ul> |
| <p>In these scenarios, <code class="docutils literal notranslate"><span class="pre">to_pandas</span></code> or <code class="docutils literal notranslate"><span class="pre">to_numpy</span></code> will be zero copy. In all |
| other scenarios, a copy will be required.</p> |
| </section> |
| <section id="reducing-memory-use-in-table-to-pandas"> |
| <h3>Reducing Memory Use in <code class="docutils literal notranslate"><span class="pre">Table.to_pandas</span></code><a class="headerlink" href="#reducing-memory-use-in-table-to-pandas" title="Permalink to this heading">#</a></h3> |
| <p>As of this writing, pandas applies a data management strategy called |
| “consolidation” to collect like-typed DataFrame columns in two-dimensional |
| NumPy arrays, referred to internally as “blocks”. We have gone to great effort |
| to construct the precise “consolidated” blocks so that pandas will not perform |
| any further allocation or copies after we hand off the data to |
| <code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code>. The obvious downside of this consolidation strategy is |
| that it forces a “memory doubling”.</p> |
| <p>To try to limit the potential effects of “memory doubling” during |
| <code class="docutils literal notranslate"><span class="pre">Table.to_pandas</span></code>, we provide a couple of options:</p> |
| <ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">split_blocks=True</span></code>, when enabled <code class="docutils literal notranslate"><span class="pre">Table.to_pandas</span></code> produces one internal |
| DataFrame “block” for each column, skipping the “consolidation” step. Note |
| that many pandas operations will trigger consolidation anyway, but the peak |
| memory use may be less than the worst case scenario of a full memory |
| doubling. As a result of this option, we are able to do zero copy conversions |
| of columns in the same cases where we can do zero copy with <code class="docutils literal notranslate"><span class="pre">Array</span></code> and |
| <code class="docutils literal notranslate"><span class="pre">ChunkedArray</span></code>.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">self_destruct=True</span></code>, this destroys the internal Arrow memory buffers in |
| each column <code class="docutils literal notranslate"><span class="pre">Table</span></code> object as they are converted to the pandas-compatible |
| representation, potentially releasing memory to the operating system as soon |
| as a column is converted. Note that this renders the calling <code class="docutils literal notranslate"><span class="pre">Table</span></code> object |
| unsafe for further use, and any further methods called will cause your Python |
| process to crash.</p></li> |
| </ul> |
| <p>Used together, the call</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(</span><span class="n">split_blocks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">self_destruct</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">del</span> <span class="n">table</span> <span class="c1"># not necessary, but a good practice</span> |
| </pre></div> |
| </div> |
| <p>will yield significantly lower memory usage in some scenarios. Without these |
| options, <code class="docutils literal notranslate"><span class="pre">to_pandas</span></code> will always double memory.</p> |
| <p>Note that <code class="docutils literal notranslate"><span class="pre">self_destruct=True</span></code> is not guaranteed to save memory. Since the |
| conversion happens column by column, memory is also freed column by column. But |
| if multiple columns share an underlying buffer, then no memory will be freed |
| until all of those columns are converted. In particular, due to implementation |
| details, data that comes from IPC or Flight is prone to this, as memory will be |
| laid out as follows:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Record</span> <span class="n">Batch</span> <span class="mi">0</span><span class="p">:</span> <span class="n">Allocation</span> <span class="mi">0</span><span class="p">:</span> <span class="n">array</span> <span class="mi">0</span> <span class="n">chunk</span> <span class="mi">0</span><span class="p">,</span> <span class="n">array</span> <span class="mi">1</span> <span class="n">chunk</span> <span class="mi">0</span><span class="p">,</span> <span class="o">...</span> |
| <span class="n">Record</span> <span class="n">Batch</span> <span class="mi">1</span><span class="p">:</span> <span class="n">Allocation</span> <span class="mi">1</span><span class="p">:</span> <span class="n">array</span> <span class="mi">0</span> <span class="n">chunk</span> <span class="mi">1</span><span class="p">,</span> <span class="n">array</span> <span class="mi">1</span> <span class="n">chunk</span> <span class="mi">1</span><span class="p">,</span> <span class="o">...</span> |
| <span class="o">...</span> |
| </pre></div> |
| </div> |
| <p>In this case, no memory can be freed until the entire table is converted, even |
| with <code class="docutils literal notranslate"><span class="pre">self_destruct=True</span></code>.</p> |
| </section> |
| </section> |
| </section> |
| |
| |
| </article> |
| |
| |
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