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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="getting-started"> |
| <span id="getstarted"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading">#</a></h1> |
| <p>Arrow manages data in arrays (<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">pyarrow.Array</span></code></a>), which can be |
| grouped in tables (<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">pyarrow.Table</span></code></a>) to represent columns of data |
| in tabular data.</p> |
| <p>Arrow also provides support for various formats to get those tabular |
| data in and out of disk and networks. Most commonly used formats are |
| Parquet (<a class="reference internal" href="parquet.html#parquet"><span class="std std-ref">Reading and Writing the Apache Parquet Format</span></a>) and the IPC format (<a class="reference internal" href="ipc.html#ipc"><span class="std std-ref">Streaming, Serialization, and IPC</span></a>).</p> |
| <section id="creating-arrays-and-tables"> |
| <h2>Creating Arrays and Tables<a class="headerlink" href="#creating-arrays-and-tables" title="Permalink to this heading">#</a></h2> |
| <p>Arrays in Arrow are collections of data of uniform type. That allows |
| Arrow to use the best performing implementation to store the data and |
| perform computations on it. So each array is meant to have data and |
| a type</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">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| |
| <span class="gp">In [2]: </span><span class="n">days</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">12</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">28</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">int8</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <p>Multiple arrays can be combined in tables to form the columns |
| in tabular data when attached to a column name</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [3]: </span><span class="n">months</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">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">int8</span><span class="p">())</span> |
| |
| <span class="gp">In [4]: </span><span class="n">years</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">1990</span><span class="p">,</span> <span class="mi">2000</span><span class="p">,</span> <span class="mi">1995</span><span class="p">,</span> <span class="mi">2000</span><span class="p">,</span> <span class="mi">1995</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">int16</span><span class="p">())</span> |
| |
| <span class="gp">In [5]: </span><span class="n">birthdays_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">days</span><span class="p">,</span> <span class="n">months</span><span class="p">,</span> <span class="n">years</span><span class="p">],</span> |
| <span class="gp"> ...: </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"days"</span><span class="p">,</span> <span class="s2">"months"</span><span class="p">,</span> <span class="s2">"years"</span><span class="p">])</span> |
| <span class="gp"> ...: </span> |
| |
| <span class="gp">In [6]: </span><span class="n">birthdays_table</span> |
| <span class="gh">Out[6]: </span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">days: int8</span> |
| <span class="go">months: int8</span> |
| <span class="go">years: int16</span> |
| <span class="gt">----</span> |
| <span class="ne">days</span>: [[1,12,17,23,28]] |
| <span class="ne">months</span>: [[1,3,5,7,1]] |
| <span class="ne">years</span>: [[1990,2000,1995,2000,1995]] |
| </pre></div> |
| </div> |
| <p>See <a class="reference internal" href="data.html#data"><span class="std std-ref">Data Types and In-Memory Data Model</span></a> for more details.</p> |
| </section> |
| <section id="saving-and-loading-tables"> |
| <h2>Saving and Loading Tables<a class="headerlink" href="#saving-and-loading-tables" title="Permalink to this heading">#</a></h2> |
| <p>Once you have tabular data, Arrow provides out of the box |
| the features to save and restore that data for common formats |
| like Parquet:</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="kn">import</span> <span class="nn">pyarrow.parquet</span> <span class="k">as</span> <span class="nn">pq</span> |
| |
| <span class="gp">In [8]: </span><span class="n">pq</span><span class="o">.</span><span class="n">write_table</span><span class="p">(</span><span class="n">birthdays_table</span><span class="p">,</span> <span class="s1">'birthdays.parquet'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Once you have your data on disk, loading it back is a single function call, |
| and Arrow is heavily optimized for memory and speed so loading |
| data will be as quick as possible</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [9]: </span><span class="n">reloaded_birthdays</span> <span class="o">=</span> <span class="n">pq</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'birthdays.parquet'</span><span class="p">)</span> |
| |
| <span class="gp">In [10]: </span><span class="n">reloaded_birthdays</span> |
| <span class="gh">Out[10]: </span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">days: int8</span> |
| <span class="go">months: int8</span> |
| <span class="go">years: int16</span> |
| <span class="gt">----</span> |
| <span class="ne">days</span>: [[1,12,17,23,28]] |
| <span class="ne">months</span>: [[1,3,5,7,1]] |
| <span class="ne">years</span>: [[1990,2000,1995,2000,1995]] |
| </pre></div> |
| </div> |
| <p>Saving and loading back data in arrow is usually done through |
| <a class="reference internal" href="parquet.html#parquet"><span class="std std-ref">Parquet</span></a>, <a class="reference internal" href="ipc.html#ipc"><span class="std std-ref">IPC format</span></a> (<a class="reference internal" href="feather.html#feather"><span class="std std-ref">Feather File Format</span></a>), |
| <a class="reference internal" href="csv.html#py-csv"><span class="std std-ref">CSV</span></a> or <a class="reference internal" href="json.html#json"><span class="std std-ref">Line-Delimited JSON</span></a> formats.</p> |
| </section> |
| <section id="performing-computations"> |
| <h2>Performing Computations<a class="headerlink" href="#performing-computations" title="Permalink to this heading">#</a></h2> |
| <p>Arrow ships with a bunch of compute functions that can be applied |
| to its arrays and tables, so through the compute functions |
| it’s possible to apply transformations to the data</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="kn">import</span> <span class="nn">pyarrow.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| |
| <span class="gp">In [12]: </span><span class="n">pc</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">birthdays_table</span><span class="p">[</span><span class="s2">"years"</span><span class="p">])</span> |
| <span class="gh">Out[12]: </span> |
| <span class="go"><pyarrow.lib.StructArray object at 0x7fa1f159d360></span> |
| <span class="go">-- is_valid: all not null</span> |
| <span class="go">-- child 0 type: int16</span> |
| <span class="go"> [</span> |
| <span class="go"> 1990,</span> |
| <span class="go"> 2000,</span> |
| <span class="go"> 1995</span> |
| <span class="go"> ]</span> |
| <span class="go">-- child 1 type: int64</span> |
| <span class="go"> [</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 2</span> |
| <span class="go"> ]</span> |
| </pre></div> |
| </div> |
| <p>See <a class="reference internal" href="compute.html#compute"><span class="std std-ref">Compute Functions</span></a> for a list of available compute functions and |
| how to use them.</p> |
| </section> |
| <section id="working-with-large-data"> |
| <h2>Working with large data<a class="headerlink" href="#working-with-large-data" title="Permalink to this heading">#</a></h2> |
| <p>Arrow also provides the <code class="xref py py-class docutils literal notranslate"><span class="pre">pyarrow.dataset</span></code> API to work with |
| large data, which will handle for you partitioning of your data in |
| smaller chunks</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="kn">import</span> <span class="nn">pyarrow.dataset</span> <span class="k">as</span> <span class="nn">ds</span> |
| |
| <span class="gp">In [14]: </span><span class="n">ds</span><span class="o">.</span><span class="n">write_dataset</span><span class="p">(</span><span class="n">birthdays_table</span><span class="p">,</span> <span class="s2">"savedir"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"parquet"</span><span class="p">,</span> |
| <span class="gp"> ....: </span> <span class="n">partitioning</span><span class="o">=</span><span class="n">ds</span><span class="o">.</span><span class="n">partitioning</span><span class="p">(</span> |
| <span class="gp"> ....: </span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span><span class="n">birthdays_table</span><span class="o">.</span><span class="n">schema</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"years"</span><span class="p">)])</span> |
| <span class="gp"> ....: </span> <span class="p">))</span> |
| <span class="gp"> ....: </span> |
| </pre></div> |
| </div> |
| <p>Loading back the partitioned dataset will detect the chunks</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [15]: </span><span class="n">birthdays_dataset</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">dataset</span><span class="p">(</span><span class="s2">"savedir"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"parquet"</span><span class="p">,</span> <span class="n">partitioning</span><span class="o">=</span><span class="p">[</span><span class="s2">"years"</span><span class="p">])</span> |
| |
| <span class="gp">In [16]: </span><span class="n">birthdays_dataset</span><span class="o">.</span><span class="n">files</span> |
| <span class="gh">Out[16]: </span> |
| <span class="go">['savedir/1990/part-0.parquet',</span> |
| <span class="go"> 'savedir/1995/part-0.parquet',</span> |
| <span class="go"> 'savedir/2000/part-0.parquet']</span> |
| </pre></div> |
| </div> |
| <p>and will lazily load chunks of data only when iterating over them</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [17]: </span><span class="kn">import</span> <span class="nn">datetime</span> |
| |
| <span class="gp">In [18]: </span><span class="n">current_year</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span><span class="o">.</span><span class="n">year</span> |
| |
| <span class="gp">In [19]: </span><span class="k">for</span> <span class="n">table_chunk</span> <span class="ow">in</span> <span class="n">birthdays_dataset</span><span class="o">.</span><span class="n">to_batches</span><span class="p">():</span> |
| <span class="gp"> ....: </span> <span class="nb">print</span><span class="p">(</span><span class="s2">"AGES"</span><span class="p">,</span> <span class="n">pc</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">current_year</span><span class="p">,</span> <span class="n">table_chunk</span><span class="p">[</span><span class="s2">"years"</span><span class="p">]))</span> |
| <span class="gp"> ....: </span> |
| <span class="go">AGES [</span> |
| <span class="go"> 34</span> |
| <span class="go">]</span> |
| <span class="go">AGES [</span> |
| <span class="go"> 29,</span> |
| <span class="go"> 29</span> |
| <span class="go">]</span> |
| <span class="go">AGES [</span> |
| <span class="go"> 24,</span> |
| <span class="go"> 24</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <p>For further details on how to work with big datasets, how to filter them, |
| how to project them, etc., refer to <a class="reference internal" href="dataset.html#dataset"><span class="std std-ref">Tabular Datasets</span></a> documentation.</p> |
| </section> |
| <section id="continuing-from-here"> |
| <h2>Continuing from here<a class="headerlink" href="#continuing-from-here" title="Permalink to this heading">#</a></h2> |
| <p>For digging further into Arrow, you might want to read the |
| <a class="reference internal" href="index.html"><span class="doc">PyArrow Documentation</span></a> itself or the |
| <a class="reference external" href="https://arrow.apache.org/cookbook/py/">Arrow Python Cookbook</a></p> |
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