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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="compute-functions"> |
| <span id="compute"></span><h1>Compute Functions<a class="headerlink" href="#compute-functions" title="Permalink to this heading">#</a></h1> |
| <p>Arrow supports logical compute operations over inputs of possibly |
| varying types.</p> |
| <p>The standard compute operations are provided by the <code class="xref py py-mod docutils literal notranslate"><span class="pre">pyarrow.compute</span></code> |
| module and can be used directly:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| <span class="gp">>>> </span><span class="n">a</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">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">pc</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> |
| <span class="go"><pyarrow.Int64Scalar: 7></span> |
| </pre></div> |
| </div> |
| <p>The grouped aggregation functions raise an exception instead |
| and need to be used through the <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table.group_by" title="pyarrow.Table.group_by"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pyarrow.Table.group_by()</span></code></a> capabilities. |
| See <a class="reference internal" href="#py-grouped-aggrs"><span class="std std-ref">Grouped Aggregations</span></a> for more details.</p> |
| <section id="standard-compute-functions"> |
| <h2>Standard Compute Functions<a class="headerlink" href="#standard-compute-functions" title="Permalink to this heading">#</a></h2> |
| <p>Many compute functions support both array (chunked or not) |
| and scalar inputs, but some will mandate either. For example, |
| <code class="docutils literal notranslate"><span class="pre">sort_indices</span></code> requires its first and only input to be an array.</p> |
| <p>Below are a few simple examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| <span class="gp">>>> </span><span class="n">a</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">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">b</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">pc</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.BooleanArray object at 0x7f686e4eef30></span> |
| <span class="go">[</span> |
| <span class="go"> false,</span> |
| <span class="go"> true,</span> |
| <span class="go"> true,</span> |
| <span class="go"> false</span> |
| <span class="go">]</span> |
| <span class="gp">>>> </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mf">7.8</span><span class="p">),</span> <span class="n">pa</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mf">9.3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pc</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> |
| <span class="go"><pyarrow.DoubleScalar: 72.54></span> |
| </pre></div> |
| </div> |
| <p>These functions can do more than just element-by-element operations. |
| Here is an example of sorting a table:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| <span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">({</span><span class="s1">'x'</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="s1">'y'</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">1</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">i</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">sort_indices</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">sort_keys</span><span class="o">=</span><span class="p">[(</span><span class="s1">'y'</span><span class="p">,</span> <span class="s1">'ascending'</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">i</span> |
| <span class="go"><pyarrow.lib.UInt64Array object at 0x7fcee5df75e8></span> |
| <span class="go">[</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 1,</span> |
| <span class="go"> 0</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <p>For a complete list of the compute functions that PyArrow provides |
| you can refer to <a class="reference internal" href="api/compute.html#api-compute"><span class="std std-ref">Compute Functions</span></a> reference.</p> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <p><a class="reference internal" href="../cpp/compute.html#compute-function-list"><span class="std std-ref">Available compute functions (C++ documentation)</span></a>.</p> |
| </div> |
| </section> |
| <section id="grouped-aggregations"> |
| <span id="py-grouped-aggrs"></span><h2>Grouped Aggregations<a class="headerlink" href="#grouped-aggregations" title="Permalink to this heading">#</a></h2> |
| <p>PyArrow supports grouped aggregations over <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> through the |
| <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table.group_by" title="pyarrow.Table.group_by"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pyarrow.Table.group_by()</span></code></a> method. |
| The method will return a grouping declaration |
| to which the hash aggregation functions can be applied:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"a"</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">"b"</span><span class="p">,</span> <span class="s2">"c"</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="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]),</span> |
| <span class="gp">... </span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"keys"</span><span class="p">,</span> <span class="s2">"values"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">t</span><span class="o">.</span><span class="n">group_by</span><span class="p">(</span><span class="s2">"keys"</span><span class="p">)</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([(</span><span class="s2">"values"</span><span class="p">,</span> <span class="s2">"sum"</span><span class="p">)])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">values_sum: int64</span> |
| <span class="go">keys: string</span> |
| <span class="go">----</span> |
| <span class="go">values_sum: [[3,7,5]]</span> |
| <span class="go">keys: [["a","b","c"]]</span> |
| </pre></div> |
| </div> |
| <p>The <code class="docutils literal notranslate"><span class="pre">"sum"</span></code> aggregation passed to the <code class="docutils literal notranslate"><span class="pre">aggregate</span></code> method in the previous |
| example is the <code class="docutils literal notranslate"><span class="pre">hash_sum</span></code> compute function.</p> |
| <p>Multiple aggregations can be performed at the same time by providing them |
| to the <code class="docutils literal notranslate"><span class="pre">aggregate</span></code> method:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"a"</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">"b"</span><span class="p">,</span> <span class="s2">"c"</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="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]),</span> |
| <span class="gp">... </span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"keys"</span><span class="p">,</span> <span class="s2">"values"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">t</span><span class="o">.</span><span class="n">group_by</span><span class="p">(</span><span class="s2">"keys"</span><span class="p">)</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s2">"values"</span><span class="p">,</span> <span class="s2">"sum"</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s2">"keys"</span><span class="p">,</span> <span class="s2">"count"</span><span class="p">)</span> |
| <span class="gp">... </span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">values_sum: int64</span> |
| <span class="go">keys_count: int64</span> |
| <span class="go">keys: string</span> |
| <span class="go">----</span> |
| <span class="go">values_sum: [[3,7,5]]</span> |
| <span class="go">keys_count: [[2,2,1]]</span> |
| <span class="go">keys: [["a","b","c"]]</span> |
| </pre></div> |
| </div> |
| <p>Aggregation options can also be provided for each aggregation function, |
| for example we can use <a class="reference internal" href="generated/pyarrow.compute.CountOptions.html#pyarrow.compute.CountOptions" title="pyarrow.compute.CountOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountOptions</span></code></a> to change how we count |
| null values:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pyarrow.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| <span class="gp">>>> </span><span class="n">table_with_nulls</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"a"</span><span class="p">,</span> <span class="s2">"a"</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="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">])</span> |
| <span class="gp">... </span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"keys"</span><span class="p">,</span> <span class="s2">"values"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table_with_nulls</span><span class="o">.</span><span class="n">group_by</span><span class="p">([</span><span class="s2">"keys"</span><span class="p">])</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s2">"values"</span><span class="p">,</span> <span class="s2">"count"</span><span class="p">,</span> <span class="n">pc</span><span class="o">.</span><span class="n">CountOptions</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s2">"all"</span><span class="p">))</span> |
| <span class="gp">... </span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">values_count: int64</span> |
| <span class="go">keys: string</span> |
| <span class="go">----</span> |
| <span class="go">values_count: [[3]]</span> |
| <span class="go">keys: [["a"]]</span> |
| <span class="gp">>>> </span><span class="n">table_with_nulls</span><span class="o">.</span><span class="n">group_by</span><span class="p">([</span><span class="s2">"keys"</span><span class="p">])</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s2">"values"</span><span class="p">,</span> <span class="s2">"count"</span><span class="p">,</span> <span class="n">pc</span><span class="o">.</span><span class="n">CountOptions</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s2">"only_valid"</span><span class="p">))</span> |
| <span class="gp">... </span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">values_count: int64</span> |
| <span class="go">keys: string</span> |
| <span class="go">----</span> |
| <span class="go">values_count: [[1]]</span> |
| <span class="go">keys: [["a"]]</span> |
| </pre></div> |
| </div> |
| <p>Following is a list of all supported grouped aggregation functions. |
| You can use them with or without the <code class="docutils literal notranslate"><span class="pre">"hash_"</span></code> prefix.</p> |
| <table class="table"> |
| <colgroup> |
| <col style="width: 20.0%" /> |
| <col style="width: 60.0%" /> |
| <col style="width: 20.0%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p>hash_all</p></td> |
| <td><p>Whether all elements in each group evaluate to true</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_any</p></td> |
| <td><p>Whether any element in each group evaluates to true</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_approximate_median</p></td> |
| <td><p>Compute approximate medians of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_count</p></td> |
| <td><p>Count the number of null / non-null values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.CountOptions.html#pyarrow.compute.CountOptions" title="pyarrow.compute.CountOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_count_all</p></td> |
| <td><p>Count the number of rows in each group</p></td> |
| <td></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_count_distinct</p></td> |
| <td><p>Count the distinct values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.CountOptions.html#pyarrow.compute.CountOptions" title="pyarrow.compute.CountOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_distinct</p></td> |
| <td><p>Keep the distinct values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.CountOptions.html#pyarrow.compute.CountOptions" title="pyarrow.compute.CountOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_first</p></td> |
| <td><p>Compute the first value in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_first_last</p></td> |
| <td><p>Compute the first and last of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_last</p></td> |
| <td><p>Compute the first value in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_list</p></td> |
| <td><p>List all values in each group</p></td> |
| <td></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_max</p></td> |
| <td><p>Compute the minimum or maximum of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_mean</p></td> |
| <td><p>Compute the mean of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_min</p></td> |
| <td><p>Compute the minimum or maximum of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_min_max</p></td> |
| <td><p>Compute the minimum and maximum of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_one</p></td> |
| <td><p>Get one value from each group</p></td> |
| <td></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_product</p></td> |
| <td><p>Compute the product of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_stddev</p></td> |
| <td><p>Compute the standard deviation of values in each group</p></td> |
| <td></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_sum</p></td> |
| <td><p>Sum values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.ScalarAggregateOptions.html#pyarrow.compute.ScalarAggregateOptions" title="pyarrow.compute.ScalarAggregateOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScalarAggregateOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-even"><td><p>hash_tdigest</p></td> |
| <td><p>Compute approximate quantiles of values in each group</p></td> |
| <td><p><a class="reference internal" href="generated/pyarrow.compute.TDigestOptions.html#pyarrow.compute.TDigestOptions" title="pyarrow.compute.TDigestOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">TDigestOptions</span></code></a></p></td> |
| </tr> |
| <tr class="row-odd"><td><p>hash_variance</p></td> |
| <td><p>Compute the variance of values in each group</p></td> |
| <td></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
| <section id="table-and-dataset-joins"> |
| <span id="py-joins"></span><h2>Table and Dataset Joins<a class="headerlink" href="#table-and-dataset-joins" title="Permalink to this heading">#</a></h2> |
| <p>Both <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> and <a class="reference internal" href="generated/pyarrow.dataset.Dataset.html#pyarrow.dataset.Dataset" title="pyarrow.dataset.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> support |
| join operations through <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table.join" title="pyarrow.Table.join"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Table.join()</span></code></a> |
| and <a class="reference internal" href="generated/pyarrow.dataset.Dataset.html#pyarrow.dataset.Dataset.join" title="pyarrow.dataset.Dataset.join"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Dataset.join()</span></code></a> methods.</p> |
| <p>The methods accept a right table or dataset that will |
| be joined to the initial one and one or more keys that |
| should be used from the two entities to perform the join.</p> |
| <p>By default a <code class="docutils literal notranslate"><span class="pre">left</span> <span class="pre">outer</span> <span class="pre">join</span></code> is performed, but it’s possible |
| to ask for any of the supported join types:</p> |
| <ul class="simple"> |
| <li><p>left semi</p></li> |
| <li><p>right semi</p></li> |
| <li><p>left anti</p></li> |
| <li><p>right anti</p></li> |
| <li><p>inner</p></li> |
| <li><p>left outer</p></li> |
| <li><p>right outer</p></li> |
| <li><p>full outer</p></li> |
| </ul> |
| <p>A basic join can be performed just by providing a table and a key |
| on which the join should be performed:</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="n">table1</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">({</span><span class="s1">'id'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">]})</span> |
| |
| <span class="n">table2</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">({</span><span class="s1">'id'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> |
| <span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="s1">'animal'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| |
| <span class="n">joined_table</span> <span class="o">=</span> <span class="n">table1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">table2</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="s2">"id"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>The result will be a new table created by joining <code class="docutils literal notranslate"><span class="pre">table1</span></code> with |
| <code class="docutils literal notranslate"><span class="pre">table2</span></code> on the <code class="docutils literal notranslate"><span class="pre">id</span></code> key with a <code class="docutils literal notranslate"><span class="pre">left</span> <span class="pre">outer</span> <span class="pre">join</span></code>:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pyarrow</span><span class="o">.</span><span class="n">Table</span> |
| <span class="nb">id</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">year</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">n_legs</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">animal</span><span class="p">:</span> <span class="n">string</span> |
| <span class="o">----</span> |
| <span class="nb">id</span><span class="p">:</span> <span class="p">[[</span><span class="mi">3</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="n">year</span><span class="p">:</span> <span class="p">[[</span><span class="mi">2019</span><span class="p">,</span><span class="mi">2020</span><span class="p">,</span><span class="mi">2022</span><span class="p">]]</span> |
| <span class="n">n_legs</span><span class="p">:</span> <span class="p">[[</span><span class="mi">5</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="n">null</span><span class="p">]]</span> |
| <span class="n">animal</span><span class="p">:</span> <span class="p">[[</span><span class="s2">"Brittle stars"</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="n">null</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>We can perform additional type of joins, like <code class="docutils literal notranslate"><span class="pre">full</span> <span class="pre">outer</span> <span class="pre">join</span></code> by |
| passing them to the <code class="docutils literal notranslate"><span class="pre">join_type</span></code> argument:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">table1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">table2</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="s1">'id'</span><span class="p">,</span> <span class="n">join_type</span><span class="o">=</span><span class="s2">"full outer"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>In that case the result would be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pyarrow</span><span class="o">.</span><span class="n">Table</span> |
| <span class="nb">id</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">year</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">n_legs</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">animal</span><span class="p">:</span> <span class="n">string</span> |
| <span class="o">----</span> |
| <span class="nb">id</span><span class="p">:</span> <span class="p">[[</span><span class="mi">3</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">4</span><span class="p">]]</span> |
| <span class="n">year</span><span class="p">:</span> <span class="p">[[</span><span class="mi">2019</span><span class="p">,</span><span class="mi">2020</span><span class="p">,</span><span class="mi">2022</span><span class="p">,</span><span class="n">null</span><span class="p">]]</span> |
| <span class="n">n_legs</span><span class="p">:</span> <span class="p">[[</span><span class="mi">5</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="mi">100</span><span class="p">]]</span> |
| <span class="n">animal</span><span class="p">:</span> <span class="p">[[</span><span class="s2">"Brittle stars"</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="s2">"Centipede"</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>It’s also possible to provide additional join keys, so that the |
| join happens on two keys instead of one. For example we can add |
| an <code class="docutils literal notranslate"><span class="pre">year</span></code> column to <code class="docutils literal notranslate"><span class="pre">table2</span></code> so that we can join on <code class="docutils literal notranslate"><span class="pre">('id',</span> <span class="pre">'year')</span></code>:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">table2_withyear</span> <span class="o">=</span> <span class="n">table2</span><span class="o">.</span><span class="n">append_column</span><span class="p">(</span><span class="s2">"year"</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">2022</span><span class="p">]))</span> |
| <span class="n">table1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">table2_withyear</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="p">[</span><span class="s2">"id"</span><span class="p">,</span> <span class="s2">"year"</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <p>The result will be a table where only entries with <code class="docutils literal notranslate"><span class="pre">id=3</span></code> and <code class="docutils literal notranslate"><span class="pre">year=2019</span></code> |
| have data, the rest will be <code class="docutils literal notranslate"><span class="pre">null</span></code>:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pyarrow</span><span class="o">.</span><span class="n">Table</span> |
| <span class="nb">id</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">year</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="n">animal</span><span class="p">:</span> <span class="n">string</span> |
| <span class="n">n_legs</span><span class="p">:</span> <span class="n">int64</span> |
| <span class="o">----</span> |
| <span class="nb">id</span><span class="p">:</span> <span class="p">[[</span><span class="mi">3</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="n">year</span><span class="p">:</span> <span class="p">[[</span><span class="mi">2019</span><span class="p">,</span><span class="mi">2020</span><span class="p">,</span><span class="mi">2022</span><span class="p">]]</span> |
| <span class="n">animal</span><span class="p">:</span> <span class="p">[[</span><span class="s2">"Brittle stars"</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="n">null</span><span class="p">]]</span> |
| <span class="n">n_legs</span><span class="p">:</span> <span class="p">[[</span><span class="mi">5</span><span class="p">,</span><span class="n">null</span><span class="p">,</span><span class="n">null</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>The same capabilities are available for <a class="reference internal" href="generated/pyarrow.dataset.Dataset.html#pyarrow.dataset.Dataset.join" title="pyarrow.dataset.Dataset.join"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Dataset.join()</span></code></a> too, so you can |
| take two datasets and join them:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyarrow.dataset</span> <span class="k">as</span> <span class="nn">ds</span> |
| |
| <span class="n">ds1</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="n">table1</span><span class="p">)</span> |
| <span class="n">ds2</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="n">table2</span><span class="p">)</span> |
| |
| <span class="n">joined_ds</span> <span class="o">=</span> <span class="n">ds1</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ds2</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s2">"id"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>The resulting dataset will be an <a class="reference internal" href="generated/pyarrow.dataset.InMemoryDataset.html#pyarrow.dataset.InMemoryDataset" title="pyarrow.dataset.InMemoryDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">InMemoryDataset</span></code></a> containing the joined data:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">joined_ds</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> |
| |
| <span class="go">pyarrow.Table</span> |
| <span class="go">id: int64</span> |
| <span class="go">year: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">id: [[3,1,2]]</span> |
| <span class="go">year: [[2019,2020,2022]]</span> |
| <span class="go">animal: [["Brittle stars",null,null]]</span> |
| <span class="go">n_legs: [[5,null,null]]</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="filtering-by-expressions"> |
| <span id="py-filter-expr"></span><h2>Filtering by Expressions<a class="headerlink" href="#filtering-by-expressions" title="Permalink to this heading">#</a></h2> |
| <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> and <a class="reference internal" href="generated/pyarrow.dataset.Dataset.html#pyarrow.dataset.Dataset" title="pyarrow.dataset.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> can |
| both be filtered using a boolean <a class="reference internal" href="generated/pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expression</span></code></a>.</p> |
| <p>The expression can be built starting from a |
| <code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.field()</span></code>. Comparisons and transformations |
| can then be applied to one or more fields to build the filter |
| expression you care about.</p> |
| <p>Most <a class="reference internal" href="#compute"><span class="std std-ref">Compute Functions</span></a> can be used to perform transformations |
| on a <code class="docutils literal notranslate"><span class="pre">field</span></code>.</p> |
| <p>For example we could build a filter to find all rows that are even |
| in column <code class="docutils literal notranslate"><span class="pre">"nums"</span></code></p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyarrow.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| <span class="n">even_filter</span> <span class="o">=</span> <span class="p">(</span><span class="n">pc</span><span class="o">.</span><span class="n">bit_wise_and</span><span class="p">(</span><span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"nums"</span><span class="p">),</span> <span class="n">pc</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span> <span class="o">==</span> <span class="n">pc</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>The filter finds even numbers by performing a bitwise and operation between the number and <code class="docutils literal notranslate"><span class="pre">1</span></code>. |
| As <code class="docutils literal notranslate"><span class="pre">1</span></code> is to <code class="docutils literal notranslate"><span class="pre">00000001</span></code> in binary form, only numbers that have the last bit set to <code class="docutils literal notranslate"><span class="pre">1</span></code> |
| will return a non-zero result from the <code class="docutils literal notranslate"><span class="pre">bit_wise_and</span></code> operation. This way we are identifying all |
| odd numbers. Given that we are interested in the even ones, we then check that the number returned |
| by the <code class="docutils literal notranslate"><span class="pre">bit_wise_and</span></code> operation equals <code class="docutils literal notranslate"><span class="pre">0</span></code>. Only the numbers where the last bit was <code class="docutils literal notranslate"><span class="pre">0</span></code> will |
| return a <code class="docutils literal notranslate"><span class="pre">0</span></code> as the result of <code class="docutils literal notranslate"><span class="pre">num</span> <span class="pre">&</span> <span class="pre">1</span></code> and as all numbers where the last bit is <code class="docutils literal notranslate"><span class="pre">0</span></code> are |
| multiples of <code class="docutils literal notranslate"><span class="pre">2</span></code> we will be filtering for the even numbers only.</p> |
| </div> |
| <p>Once we have our filter, we can provide it to the <a class="reference internal" href="generated/pyarrow.Table.html#pyarrow.Table.filter" title="pyarrow.Table.filter"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Table.filter()</span></code></a> method |
| to filter our table only for the matching rows:</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="s1">'nums'</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="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="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'chars'</span><span class="p">:</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">"d"</span><span class="p">,</span> <span class="s2">"e"</span><span class="p">,</span> <span class="s2">"f"</span><span class="p">,</span> <span class="s2">"g"</span><span class="p">,</span> <span class="s2">"h"</span><span class="p">,</span> <span class="s2">"i"</span><span class="p">,</span> <span class="s2">"l"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">even_filter</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">nums: int64</span> |
| <span class="go">chars: string</span> |
| <span class="go">----</span> |
| <span class="go">nums: [[2,4,6,8,10]]</span> |
| <span class="go">chars: [["b","d","f","h","l"]]</span> |
| </pre></div> |
| </div> |
| <p>Multiple filters can be joined using <code class="docutils literal notranslate"><span class="pre">&</span></code>, <code class="docutils literal notranslate"><span class="pre">|</span></code>, <code class="docutils literal notranslate"><span class="pre">~</span></code> to perform <code class="docutils literal notranslate"><span class="pre">and</span></code>, <code class="docutils literal notranslate"><span class="pre">or</span></code> |
| and <code class="docutils literal notranslate"><span class="pre">not</span></code> operations. For example using <code class="docutils literal notranslate"><span class="pre">~even_filter</span></code> will actually end up filtering |
| for all numbers that are odd:</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">filter</span><span class="p">(</span><span class="o">~</span><span class="n">even_filter</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">nums: int64</span> |
| <span class="go">chars: string</span> |
| <span class="go">----</span> |
| <span class="go">nums: [[1,3,5,7,9]]</span> |
| <span class="go">chars: [["a","c","e","g","i"]]</span> |
| </pre></div> |
| </div> |
| <p>and we could build a filter that finds all even numbers greater than 5 by combining |
| our <code class="docutils literal notranslate"><span class="pre">even_filter</span></code> with a <code class="docutils literal notranslate"><span class="pre">pc.field("nums")</span> <span class="pre">></span> <span class="pre">5</span></code> filter:</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">filter</span><span class="p">(</span><span class="n">even_filter</span> <span class="o">&</span> <span class="p">(</span><span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"nums"</span><span class="p">)</span> <span class="o">></span> <span class="mi">5</span><span class="p">))</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">nums: int64</span> |
| <span class="go">chars: string</span> |
| <span class="go">----</span> |
| <span class="go">nums: [[6,8,10]]</span> |
| <span class="go">chars: [["f","h","l"]]</span> |
| </pre></div> |
| </div> |
| <p><a class="reference internal" href="generated/pyarrow.dataset.Dataset.html#pyarrow.dataset.Dataset" title="pyarrow.dataset.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> can similarly be filtered with the <a class="reference internal" href="generated/pyarrow.dataset.Dataset.html#pyarrow.dataset.Dataset.filter" title="pyarrow.dataset.Dataset.filter"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Dataset.filter()</span></code></a> method. |
| The method will return an instance of <a class="reference internal" href="generated/pyarrow.dataset.Dataset.html#pyarrow.dataset.Dataset" title="pyarrow.dataset.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a> which will lazily |
| apply the filter as soon as actual data of the dataset is accessed:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">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="n">table</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">filtered</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"nums"</span><span class="p">)</span> <span class="o"><</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"nums"</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">filtered</span><span class="o">.</span><span class="n">to_table</span><span class="p">()</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">nums: int64</span> |
| <span class="go">chars: string</span> |
| <span class="go">----</span> |
| <span class="go">nums: [[3,4]]</span> |
| <span class="go">chars: [["c","d"]]</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="user-defined-functions"> |
| <h2>User-Defined Functions<a class="headerlink" href="#user-defined-functions" title="Permalink to this heading">#</a></h2> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>This API is <strong>experimental</strong>.</p> |
| </div> |
| <p>PyArrow allows defining and registering custom compute functions. |
| These functions can then be called from Python as well as C++ (and potentially |
| any other implementation wrapping Arrow C++, such as the R <code class="docutils literal notranslate"><span class="pre">arrow</span></code> package) |
| using their registered function name.</p> |
| <p>UDF support is limited to scalar functions. A scalar function is a function which |
| executes elementwise operations on arrays or scalars. In general, the output of a |
| scalar function does not depend on the order of values in the arguments. Note that |
| such functions have a rough correspondence to the functions used in SQL expressions, |
| or to NumPy <a class="reference external" href="https://numpy.org/doc/stable/reference/ufuncs.html">universal functions</a>.</p> |
| <p>To register a UDF, a function name, function docs, input types and |
| output type need to be defined. Using <a class="reference internal" href="generated/pyarrow.compute.register_scalar_function.html#pyarrow.compute.register_scalar_function" title="pyarrow.compute.register_scalar_function"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.register_scalar_function()</span></code></a>,</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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">pyarrow.compute</span> <span class="k">as</span> <span class="nn">pc</span> |
| |
| <span class="n">function_name</span> <span class="o">=</span> <span class="s2">"numpy_gcd"</span> |
| <span class="n">function_docs</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s2">"summary"</span><span class="p">:</span> <span class="s2">"Calculates the greatest common divisor"</span><span class="p">,</span> |
| <span class="s2">"description"</span><span class="p">:</span> |
| <span class="s2">"Given 'x' and 'y' find the greatest number that divides</span><span class="se">\n</span><span class="s2">"</span> |
| <span class="s2">"evenly into both x and y."</span> |
| <span class="p">}</span> |
| |
| <span class="n">input_types</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s2">"x"</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="s2">"y"</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="p">}</span> |
| |
| <span class="n">output_type</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">to_np</span><span class="p">(</span><span class="n">val</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">Scalar</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">val</span><span class="o">.</span><span class="n">as_py</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">gcd_numpy</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span> |
| <span class="n">np_x</span> <span class="o">=</span> <span class="n">to_np</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">np_y</span> <span class="o">=</span> <span class="n">to_np</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">gcd</span><span class="p">(</span><span class="n">np_x</span><span class="p">,</span> <span class="n">np_y</span><span class="p">))</span> |
| |
| <span class="n">pc</span><span class="o">.</span><span class="n">register_scalar_function</span><span class="p">(</span><span class="n">gcd_numpy</span><span class="p">,</span> |
| <span class="n">function_name</span><span class="p">,</span> |
| <span class="n">function_docs</span><span class="p">,</span> |
| <span class="n">input_types</span><span class="p">,</span> |
| <span class="n">output_type</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>The implementation of a user-defined function always takes a first <em>context</em> |
| parameter (named <code class="docutils literal notranslate"><span class="pre">ctx</span></code> in the example above) which is an instance of |
| <a class="reference internal" href="generated/pyarrow.compute.UdfContext.html#pyarrow.compute.UdfContext" title="pyarrow.compute.UdfContext"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyarrow.compute.UdfContext</span></code></a>. |
| This context exposes several useful attributes, particularly a |
| <a class="reference internal" href="generated/pyarrow.compute.UdfContext.html#pyarrow.compute.UdfContext.memory_pool" title="pyarrow.compute.UdfContext.memory_pool"><code class="xref py py-attr docutils literal notranslate"><span class="pre">memory_pool</span></code></a> to be used for |
| allocations in the context of the user-defined function.</p> |
| <p>You can call a user-defined function directly using <code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.call_function()</span></code>:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pc</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span><span class="s2">"numpy_gcd"</span><span class="p">,</span> <span class="p">[</span><span class="n">pa</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mi">27</span><span class="p">),</span> <span class="n">pa</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mi">63</span><span class="p">)])</span> |
| <span class="go"><pyarrow.Int64Scalar: 9></span> |
| <span class="gp">>>> </span><span class="n">pc</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span><span class="s2">"numpy_gcd"</span><span class="p">,</span> <span class="p">[</span><span class="n">pa</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mi">27</span><span class="p">),</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">81</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">])])</span> |
| <span class="go"><pyarrow.lib.Int64Array object at 0x7fcfa0e7b100></span> |
| <span class="go">[</span> |
| <span class="go"> 27,</span> |
| <span class="go"> 3,</span> |
| <span class="go"> 1</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <section id="working-with-datasets"> |
| <h3>Working with Datasets<a class="headerlink" href="#working-with-datasets" title="Permalink to this heading">#</a></h3> |
| <p>More generally, user-defined functions are usable everywhere a compute function |
| can be referred by its name. For example, they can be called on a dataset’s |
| column using <code class="xref py py-meth docutils literal notranslate"><span class="pre">Expression._call()</span></code>.</p> |
| <p>Consider an instance where the data is in a table and we want to compute |
| the GCD of one column with the scalar value 30. We will be re-using the |
| “numpy_gcd” user-defined function that was created above:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </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">>>> </span><span class="n">data_table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">({</span><span class="s1">'category'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">],</span> <span class="s1">'value'</span><span class="p">:</span> <span class="p">[</span><span class="mi">90</span><span class="p">,</span> <span class="mi">630</span><span class="p">,</span> <span class="mi">1827</span><span class="p">,</span> <span class="mi">2709</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">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="n">data_table</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">func_args</span> <span class="o">=</span> <span class="p">[</span><span class="n">pc</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="mi">30</span><span class="p">),</span> <span class="n">ds</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"value"</span><span class="p">)]</span> |
| <span class="gp">>>> </span><span class="n">dataset</span><span class="o">.</span><span class="n">to_table</span><span class="p">(</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">{</span> |
| <span class="gp">... </span> <span class="s1">'gcd_value'</span><span class="p">:</span> <span class="n">ds</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span><span class="o">.</span><span class="n">_call</span><span class="p">(</span><span class="s2">"numpy_gcd"</span><span class="p">,</span> <span class="n">func_args</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="s1">'value'</span><span class="p">:</span> <span class="n">ds</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'value'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="s1">'category'</span><span class="p">:</span> <span class="n">ds</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'category'</span><span class="p">)</span> |
| <span class="gp">... </span> <span class="p">})</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">gcd_value: int64</span> |
| <span class="go">value: int64</span> |
| <span class="go">category: string</span> |
| <span class="go">----</span> |
| <span class="go">gcd_value: [[30,30,3,3]]</span> |
| <span class="go">value: [[90,630,1827,2709]]</span> |
| <span class="go">category: [["A","B","C","D"]]</span> |
| </pre></div> |
| </div> |
| <p>Note that <code class="docutils literal notranslate"><span class="pre">ds.field('')._call(...)</span></code> returns a <code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.Expression()</span></code>. |
| The arguments passed to this function call are expressions, not scalar values |
| (notice the difference between <a class="reference internal" href="generated/pyarrow.scalar.html#pyarrow.scalar" title="pyarrow.scalar"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.scalar()</span></code></a> and <code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.scalar()</span></code>, |
| the latter produces an expression). |
| This expression is evaluated when the projection operator executes it.</p> |
| <section id="projection-expressions"> |
| <h4>Projection Expressions<a class="headerlink" href="#projection-expressions" title="Permalink to this heading">#</a></h4> |
| <p>In the above example we used an expression to add a new column (<code class="docutils literal notranslate"><span class="pre">gcd_value</span></code>) |
| to our table. Adding new, dynamically computed, columns to a table is known as “projection” |
| and there are limitations on what kinds of functions can be used in projection expressions. |
| A projection function must emit a single output value for each input row. That output value |
| should be calculated entirely from the input row and should not depend on any other row. |
| For example, the “numpy_gcd” function that we’ve been using as an example above is a valid |
| function to use in a projection. A “cumulative sum” function would not be a valid function |
| since the result of each input row depends on the rows that came before. A “drop nulls” |
| function would also be invalid because it doesn’t emit a value for some rows.</p> |
| </section> |
| </section> |
| </section> |
| </section> |
| |
| |
| </article> |
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