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| <section id="aggregation"> |
| <span id="id1"></span><h1>Aggregation<a class="headerlink" href="#aggregation" title="Link to this heading">¶</a></h1> |
| <p>An aggregate or aggregation is a function where the values of multiple rows are processed together |
| to form a single summary value. For performing an aggregation, DataFusion provides the |
| <a class="reference internal" href="../../autoapi/datafusion/dataframe/index.html#datafusion.dataframe.DataFrame.aggregate" title="datafusion.dataframe.DataFrame.aggregate"><code class="xref py py-func docutils literal notranslate"><span class="pre">aggregate()</span></code></a></p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="kn">from</span><span class="w"> </span><span class="nn">datafusion</span><span class="w"> </span><span class="kn">import</span> <span class="n">SessionContext</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">lit</span><span class="p">,</span> <span class="n">functions</span> <span class="k">as</span> <span class="n">f</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">2</span><span class="p">]:</span> <span class="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</span><span class="p">()</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">3</span><span class="p">]:</span> <span class="n">df</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"pokemon.csv"</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">4</span><span class="p">]:</span> <span class="n">col_type_1</span> <span class="o">=</span> <span class="n">col</span><span class="p">(</span><span class="s1">'"Type 1"'</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">5</span><span class="p">]:</span> <span class="n">col_type_2</span> <span class="o">=</span> <span class="n">col</span><span class="p">(</span><span class="s1">'"Type 2"'</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">6</span><span class="p">]:</span> <span class="n">col_speed</span> <span class="o">=</span> <span class="n">col</span><span class="p">(</span><span class="s1">'"Speed"'</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">7</span><span class="p">]:</span> <span class="n">col_attack</span> <span class="o">=</span> <span class="n">col</span><span class="p">(</span><span class="s1">'"Attack"'</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">8</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span> |
| <span class="o">...</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">approx_distinct</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Count"</span><span class="p">),</span> |
| <span class="o">...</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">approx_median</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Median Speed"</span><span class="p">),</span> |
| <span class="o">...</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">approx_percentile_cont</span><span class="p">(</span><span class="n">col_speed</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"90% Speed"</span><span class="p">)])</span> |
| <span class="o">...</span><span class="p">:</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">8</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+-------+--------------+-----------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Count</span> <span class="o">|</span> <span class="n">Median</span> <span class="n">Speed</span> <span class="o">|</span> <span class="mi">90</span><span class="o">%</span> <span class="n">Speed</span> <span class="o">|</span> |
| <span class="o">+----------+-------+--------------+-----------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="mi">11</span> <span class="o">|</span> <span class="mi">63</span> <span class="o">|</span> <span class="mi">107</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="mi">12</span> <span class="o">|</span> <span class="mi">55</span> <span class="o">|</span> <span class="mi">85</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="mi">8</span> <span class="o">|</span> <span class="mi">100</span> <span class="o">|</span> <span class="mi">136</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="mi">2</span> <span class="o">|</span> <span class="mi">47</span> <span class="o">|</span> <span class="mi">60</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="mi">20</span> <span class="o">|</span> <span class="mi">71</span> <span class="o">|</span> <span class="mi">110</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="mi">2</span> <span class="o">|</span> <span class="mi">90</span> <span class="o">|</span> <span class="mi">95</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="mi">8</span> <span class="o">|</span> <span class="mi">55</span> <span class="o">|</span> <span class="mi">80</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="mi">8</span> <span class="o">|</span> <span class="mi">91</span> <span class="o">|</span> <span class="mi">100</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="mi">21</span> <span class="o">|</span> <span class="mi">70</span> <span class="o">|</span> <span class="mi">90</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="mi">7</span> <span class="o">|</span> <span class="mi">40</span> <span class="o">|</span> <span class="mi">112</span> <span class="o">|</span> |
| <span class="o">+----------+-------+--------------+-----------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>When the <code class="code docutils literal notranslate"><span class="pre">group_by</span></code> list is empty the aggregation is done over the whole <a class="reference internal" href="../../autoapi/datafusion/dataframe/index.html#datafusion.dataframe.DataFrame" title="datafusion.dataframe.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a>. |
| For grouping the <code class="code docutils literal notranslate"><span class="pre">group_by</span></code> list must contain at least one column.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">9</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span> |
| <span class="o">...</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Max Speed"</span><span class="p">),</span> |
| <span class="o">...</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">avg</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Avg Speed"</span><span class="p">),</span> |
| <span class="o">...</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Min Speed"</span><span class="p">)])</span> |
| <span class="o">...</span><span class="p">:</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">9</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+-----------+--------------------+-----------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Max</span> <span class="n">Speed</span> <span class="o">|</span> <span class="n">Avg</span> <span class="n">Speed</span> <span class="o">|</span> <span class="n">Min</span> <span class="n">Speed</span> <span class="o">|</span> |
| <span class="o">+----------+-----------+--------------------+-----------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="mi">145</span> <span class="o">|</span> <span class="mf">66.78571428571429</span> <span class="o">|</span> <span class="mi">25</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="mi">90</span> <span class="o">|</span> <span class="mf">58.785714285714285</span> <span class="o">|</span> <span class="mi">25</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="mi">140</span> <span class="o">|</span> <span class="mf">98.88888888888889</span> <span class="o">|</span> <span class="mi">45</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="mi">60</span> <span class="o">|</span> <span class="mf">47.5</span> <span class="o">|</span> <span class="mi">35</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="mi">121</span> <span class="o">|</span> <span class="mf">72.75</span> <span class="o">|</span> <span class="mi">20</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="mi">95</span> <span class="o">|</span> <span class="mf">90.0</span> <span class="o">|</span> <span class="mi">85</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="mi">80</span> <span class="o">|</span> <span class="mf">54.23076923076923</span> <span class="o">|</span> <span class="mi">30</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="mi">105</span> <span class="o">|</span> <span class="mf">86.28571428571429</span> <span class="o">|</span> <span class="mi">60</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="mi">115</span> <span class="o">|</span> <span class="mf">67.25806451612904</span> <span class="o">|</span> <span class="mi">15</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="mi">120</span> <span class="o">|</span> <span class="mf">58.125</span> <span class="o">|</span> <span class="mi">25</span> <span class="o">|</span> |
| <span class="o">+----------+-----------+--------------------+-----------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>More than one column can be used for grouping</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">10</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">,</span> <span class="n">col_type_2</span><span class="p">],</span> <span class="p">[</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Max Speed"</span><span class="p">),</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">avg</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Avg Speed"</span><span class="p">),</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Min Speed"</span><span class="p">)])</span> |
| <span class="o">....</span><span class="p">:</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">10</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+---------+-----------+--------------------+-----------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Type</span> <span class="mi">2</span> <span class="o">|</span> <span class="n">Max</span> <span class="n">Speed</span> <span class="o">|</span> <span class="n">Avg</span> <span class="n">Speed</span> <span class="o">|</span> <span class="n">Min</span> <span class="n">Speed</span> <span class="o">|</span> |
| <span class="o">+----------+---------+-----------+--------------------+-----------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="o">|</span> <span class="mi">85</span> <span class="o">|</span> <span class="mf">53.333333333333336</span> <span class="o">|</span> <span class="mi">30</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="n">Flying</span> <span class="o">|</span> <span class="mi">121</span> <span class="o">|</span> <span class="mf">83.77777777777777</span> <span class="o">|</span> <span class="mi">56</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="o">|</span> <span class="mi">80</span> <span class="o">|</span> <span class="mf">51.7</span> <span class="o">|</span> <span class="mi">25</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="o">|</span> <span class="mi">140</span> <span class="o">|</span> <span class="mf">112.5</span> <span class="o">|</span> <span class="mi">90</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="o">|</span> <span class="mi">60</span> <span class="o">|</span> <span class="mf">47.5</span> <span class="o">|</span> <span class="mi">35</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="mi">70</span> <span class="o">|</span> <span class="mf">66.66666666666667</span> <span class="o">|</span> <span class="mi">60</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="n">Psychic</span> <span class="o">|</span> <span class="mi">95</span> <span class="o">|</span> <span class="mf">95.0</span> <span class="o">|</span> <span class="mi">95</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="n">Flying</span> <span class="o">|</span> <span class="mi">85</span> <span class="o">|</span> <span class="mf">85.0</span> <span class="o">|</span> <span class="mi">85</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="n">Flying</span> <span class="o">|</span> <span class="mi">100</span> <span class="o">|</span> <span class="mf">96.66666666666667</span> <span class="o">|</span> <span class="mi">90</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="n">Dragon</span> <span class="o">|</span> <span class="mi">100</span> <span class="o">|</span> <span class="mf">100.0</span> <span class="o">|</span> <span class="mi">100</span> <span class="o">|</span> |
| <span class="o">+----------+---------+-----------+--------------------+-----------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <section id="setting-parameters"> |
| <h2>Setting Parameters<a class="headerlink" href="#setting-parameters" title="Link to this heading">¶</a></h2> |
| <p>Each of the built in aggregate functions provides arguments for the parameters that affect their |
| operation. These can also be overridden using the builder approach to setting any of the following |
| parameters. When you use the builder, you must call <code class="docutils literal notranslate"><span class="pre">build()</span></code> to finish. For example, these two |
| expressions are equivalent.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">11</span><span class="p">]:</span> <span class="n">first_1</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">first_value</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"a"</span><span class="p">),</span> <span class="n">order_by</span><span class="o">=</span><span class="p">[</span><span class="n">col</span><span class="p">(</span><span class="s2">"a"</span><span class="p">)])</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">12</span><span class="p">]:</span> <span class="n">first_2</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">first_value</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"a"</span><span class="p">))</span><span class="o">.</span><span class="n">order_by</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"a"</span><span class="p">))</span><span class="o">.</span><span class="n">build</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <section id="ordering"> |
| <h3>Ordering<a class="headerlink" href="#ordering" title="Link to this heading">¶</a></h3> |
| <p>You can control the order in which rows are processed by window functions by providing |
| a list of <code class="docutils literal notranslate"><span class="pre">order_by</span></code> functions for the <code class="docutils literal notranslate"><span class="pre">order_by</span></code> parameter. In the following example, we |
| sort the Pokemon by their attack in increasing order and take the first value, which gives us the |
| Pokemon with the smallest attack value in each <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">1</span></code>.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">13</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span> |
| <span class="o">....</span><span class="p">:</span> <span class="p">[</span><span class="n">col</span><span class="p">(</span><span class="s1">'"Type 1"'</span><span class="p">)],</span> |
| <span class="o">....</span><span class="p">:</span> <span class="p">[</span><span class="n">f</span><span class="o">.</span><span class="n">first_value</span><span class="p">(</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">col</span><span class="p">(</span><span class="s1">'"Name"'</span><span class="p">),</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">order_by</span><span class="o">=</span><span class="p">[</span><span class="n">col</span><span class="p">(</span><span class="s1">'"Attack"'</span><span class="p">)</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span> |
| <span class="o">....</span><span class="p">:</span> <span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Smallest Attack"</span><span class="p">)</span> |
| <span class="o">....</span><span class="p">:</span> <span class="p">])</span> |
| <span class="o">....</span><span class="p">:</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">13</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+-----------------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Smallest</span> <span class="n">Attack</span> <span class="o">|</span> |
| <span class="o">+----------+-----------------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="n">Metapod</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="n">Zubat</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="n">Voltorb</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="n">Clefairy</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="n">Chansey</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="n">Jynx</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="n">Exeggcute</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="n">Vulpix</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="n">Magikarp</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="n">Cubone</span> <span class="o">|</span> |
| <span class="o">+----------+-----------------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="distinct"> |
| <h3>Distinct<a class="headerlink" href="#distinct" title="Link to this heading">¶</a></h3> |
| <p>When you set the parameter <code class="docutils literal notranslate"><span class="pre">distinct</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code>, then unique values will only be evaluated one |
| time each. Suppose we want to create an array of all of the <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">2</span></code> for each <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">1</span></code> of our |
| Pokemon set. Since there will be many entries of <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">2</span></code> we only one each distinct value.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">14</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span><span class="n">f</span><span class="o">.</span><span class="n">array_agg</span><span class="p">(</span><span class="n">col_type_2</span><span class="p">,</span> <span class="n">distinct</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Type 2 List"</span><span class="p">)])</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">14</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+--------------------------------------------------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Type</span> <span class="mi">2</span> <span class="n">List</span> <span class="o">|</span> |
| <span class="o">+----------+--------------------------------------------------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="p">,</span> <span class="n">Poison</span><span class="p">,</span> <span class="n">Grass</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="p">[,</span> <span class="n">Ground</span><span class="p">,</span> <span class="n">Flying</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="p">,</span> <span class="n">Steel</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="p">[]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="p">,</span> <span class="n">Fairy</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="p">[,</span> <span class="n">Poison</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="p">[</span><span class="n">Dragon</span><span class="p">,</span> <span class="p">,</span> <span class="n">Flying</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="p">[</span><span class="n">Poison</span><span class="p">,</span> <span class="n">Flying</span><span class="p">,</span> <span class="n">Dark</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">,</span> <span class="n">Ice</span><span class="p">,</span> <span class="n">Fighting</span><span class="p">,</span> <span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="p">[,</span> <span class="n">Rock</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">+----------+--------------------------------------------------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>In the output of the above we can see that there are some <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">1</span></code> for which the <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">2</span></code> entry |
| is <code class="docutils literal notranslate"><span class="pre">null</span></code>. In reality, we probably want to filter those out. We can do this in two ways. First, |
| we can filter DataFrame rows that have no <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">2</span></code>. If we do this, we might have some <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">1</span></code> |
| entries entirely removed. The second is we can use the <code class="docutils literal notranslate"><span class="pre">filter</span></code> argument described below.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">15</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col_type_2</span><span class="o">.</span><span class="n">is_not_null</span><span class="p">())</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span><span class="n">f</span><span class="o">.</span><span class="n">array_agg</span><span class="p">(</span><span class="n">col_type_2</span><span class="p">,</span> <span class="n">distinct</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Type 2 List"</span><span class="p">)])</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">15</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+------------------------------------------------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Type</span> <span class="mi">2</span> <span class="n">List</span> <span class="o">|</span> |
| <span class="o">+----------+------------------------------------------------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Grass</span><span class="p">,</span> <span class="n">Poison</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="p">[</span><span class="n">Ground</span><span class="p">,</span> <span class="n">Flying</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Steel</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="p">[</span><span class="n">Fairy</span><span class="p">,</span> <span class="n">Flying</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="p">[</span><span class="n">Poison</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="p">[</span><span class="n">Dragon</span><span class="p">,</span> <span class="n">Flying</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Poison</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">,</span> <span class="n">Fighting</span><span class="p">,</span> <span class="n">Ice</span><span class="p">,</span> <span class="n">Dark</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Rock</span> <span class="o">|</span> <span class="p">[</span><span class="n">Water</span><span class="p">,</span> <span class="n">Flying</span><span class="p">,</span> <span class="n">Ground</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ghost</span> <span class="o">|</span> <span class="p">[</span><span class="n">Poison</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">+----------+------------------------------------------------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">16</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span><span class="n">f</span><span class="o">.</span><span class="n">array_agg</span><span class="p">(</span><span class="n">col_type_2</span><span class="p">,</span> <span class="n">distinct</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="n">col_type_2</span><span class="o">.</span><span class="n">is_not_null</span><span class="p">())</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Type 2 List"</span><span class="p">)])</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">16</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+------------------------------------------------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Type</span> <span class="mi">2</span> <span class="n">List</span> <span class="o">|</span> |
| <span class="o">+----------+------------------------------------------------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Grass</span><span class="p">,</span> <span class="n">Poison</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="p">[</span><span class="n">Ground</span><span class="p">,</span> <span class="n">Flying</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Steel</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Fairy</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="p">[</span><span class="n">Psychic</span><span class="p">,</span> <span class="n">Poison</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="p">[</span><span class="n">Flying</span><span class="p">,</span> <span class="n">Dragon</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="p">[</span><span class="n">Poison</span><span class="p">,</span> <span class="n">Ice</span><span class="p">,</span> <span class="n">Flying</span><span class="p">,</span> <span class="n">Fighting</span><span class="p">,</span> <span class="n">Psychic</span><span class="p">,</span> <span class="n">Dark</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="p">[</span><span class="n">Rock</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">+----------+------------------------------------------------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>Which approach you take should depend on your use case.</p> |
| </section> |
| <section id="null-treatment"> |
| <h3>Null Treatment<a class="headerlink" href="#null-treatment" title="Link to this heading">¶</a></h3> |
| <p>This option allows you to either respect or ignore null values.</p> |
| <p>One common usage for handling nulls is the case where you want to find the first value within a |
| partition. By setting the null treatment to ignore nulls, we can find the first non-null value |
| in our partition.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">17</span><span class="p">]:</span> <span class="kn">from</span><span class="w"> </span><span class="nn">datafusion.common</span><span class="w"> </span><span class="kn">import</span> <span class="n">NullTreatment</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">18</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">first_value</span><span class="p">(</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">col_type_2</span><span class="p">,</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">order_by</span><span class="o">=</span><span class="p">[</span><span class="n">col_attack</span><span class="p">],</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">null_treatment</span><span class="o">=</span><span class="n">NullTreatment</span><span class="o">.</span><span class="n">RESPECT_NULLS</span> |
| <span class="o">....</span><span class="p">:</span> <span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Lowest Attack Type 2"</span><span class="p">)])</span> |
| <span class="o">....</span><span class="p">:</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">18</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+----------------------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Lowest</span> <span class="n">Attack</span> <span class="n">Type</span> <span class="mi">2</span> <span class="o">|</span> |
| <span class="o">+----------+----------------------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="n">Flying</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="n">Psychic</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="n">Psychic</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">+----------+----------------------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">19</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">first_value</span><span class="p">(</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">col_type_2</span><span class="p">,</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">order_by</span><span class="o">=</span><span class="p">[</span><span class="n">col_attack</span><span class="p">],</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">null_treatment</span><span class="o">=</span><span class="n">NullTreatment</span><span class="o">.</span><span class="n">IGNORE_NULLS</span> |
| <span class="o">....</span><span class="p">:</span> <span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Lowest Attack Type 2"</span><span class="p">)])</span> |
| <span class="o">....</span><span class="p">:</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">19</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+----------------------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Lowest</span> <span class="n">Attack</span> <span class="n">Type</span> <span class="mi">2</span> <span class="o">|</span> |
| <span class="o">+----------+----------------------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="n">Flying</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="n">Steel</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="n">Flying</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="n">Psychic</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="n">Psychic</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="n">Flying</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="n">Rock</span> <span class="o">|</span> |
| <span class="o">+----------+----------------------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="filter"> |
| <h3>Filter<a class="headerlink" href="#filter" title="Link to this heading">¶</a></h3> |
| <p>Using the filter option is useful for filtering results to include in the aggregate function. It can |
| be seen in the example above on how this can be useful to only filter rows evaluated by the |
| aggregate function without filtering rows from the entire DataFrame.</p> |
| <p>Filter takes a single expression.</p> |
| <p>Suppose we want to find the speed values for only Pokemon that have low Attack values.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">20</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">aggregate</span><span class="p">([</span><span class="n">col_type_1</span><span class="p">],</span> <span class="p">[</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">avg</span><span class="p">(</span><span class="n">col_speed</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Avg Speed All"</span><span class="p">),</span> |
| <span class="o">....</span><span class="p">:</span> <span class="n">f</span><span class="o">.</span><span class="n">avg</span><span class="p">(</span><span class="n">col_speed</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="n">col_attack</span> <span class="o"><</span> <span class="n">lit</span><span class="p">(</span><span class="mi">50</span><span class="p">))</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Avg Speed Low Attack"</span><span class="p">)])</span> |
| <span class="o">....</span><span class="p">:</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">20</span><span class="p">]:</span> |
| <span class="n">DataFrame</span><span class="p">()</span> |
| <span class="o">+----------+--------------------+----------------------+</span> |
| <span class="o">|</span> <span class="n">Type</span> <span class="mi">1</span> <span class="o">|</span> <span class="n">Avg</span> <span class="n">Speed</span> <span class="n">All</span> <span class="o">|</span> <span class="n">Avg</span> <span class="n">Speed</span> <span class="n">Low</span> <span class="n">Attack</span> <span class="o">|</span> |
| <span class="o">+----------+--------------------+----------------------+</span> |
| <span class="o">|</span> <span class="n">Bug</span> <span class="o">|</span> <span class="mf">66.78571428571429</span> <span class="o">|</span> <span class="mf">46.0</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Poison</span> <span class="o">|</span> <span class="mf">58.785714285714285</span> <span class="o">|</span> <span class="mf">48.0</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Electric</span> <span class="o">|</span> <span class="mf">98.88888888888889</span> <span class="o">|</span> <span class="mf">72.5</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fairy</span> <span class="o">|</span> <span class="mf">47.5</span> <span class="o">|</span> <span class="mf">35.0</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Normal</span> <span class="o">|</span> <span class="mf">72.75</span> <span class="o">|</span> <span class="mf">52.8</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ice</span> <span class="o">|</span> <span class="mf">90.0</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Grass</span> <span class="o">|</span> <span class="mf">54.23076923076923</span> <span class="o">|</span> <span class="mf">42.5</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Fire</span> <span class="o">|</span> <span class="mf">86.28571428571429</span> <span class="o">|</span> <span class="mf">65.0</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Water</span> <span class="o">|</span> <span class="mf">67.25806451612904</span> <span class="o">|</span> <span class="mf">63.833333333333336</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">Ground</span> <span class="o">|</span> <span class="mf">58.125</span> <span class="o">|</span> <span class="o">|</span> |
| <span class="o">+----------+--------------------+----------------------+</span> |
| <span class="n">Data</span> <span class="n">truncated</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </section> |
| </section> |
| <section id="aggregate-functions"> |
| <h2>Aggregate Functions<a class="headerlink" href="#aggregate-functions" title="Link to this heading">¶</a></h2> |
| <p>The available aggregate functions are:</p> |
| <ol class="arabic simple"> |
| <li><dl class="simple"> |
| <dt>Comparison Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.min" title="datafusion.functions.min"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.min()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.max" title="datafusion.functions.max"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.max()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>Math Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.sum" title="datafusion.functions.sum"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.sum()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.avg" title="datafusion.functions.avg"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.avg()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.median" title="datafusion.functions.median"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.median()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>Array Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.array_agg" title="datafusion.functions.array_agg"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.array_agg()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>Logical Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.bit_and" title="datafusion.functions.bit_and"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.bit_and()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.bit_or" title="datafusion.functions.bit_or"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.bit_or()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.bit_xor" title="datafusion.functions.bit_xor"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.bit_xor()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.bool_and" title="datafusion.functions.bool_and"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.bool_and()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.bool_or" title="datafusion.functions.bool_or"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.bool_or()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>Statistical Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.count" title="datafusion.functions.count"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.count()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.corr" title="datafusion.functions.corr"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.corr()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.covar_samp" title="datafusion.functions.covar_samp"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.covar_samp()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.covar_pop" title="datafusion.functions.covar_pop"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.covar_pop()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.stddev" title="datafusion.functions.stddev"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.stddev()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.stddev_pop" title="datafusion.functions.stddev_pop"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.stddev_pop()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.var_samp" title="datafusion.functions.var_samp"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.var_samp()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.var_pop" title="datafusion.functions.var_pop"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.var_pop()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>Linear Regression Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_count" title="datafusion.functions.regr_count"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_count()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_slope" title="datafusion.functions.regr_slope"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_slope()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_intercept" title="datafusion.functions.regr_intercept"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_intercept()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_r2" title="datafusion.functions.regr_r2"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_r2()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_avgx" title="datafusion.functions.regr_avgx"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_avgx()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_avgy" title="datafusion.functions.regr_avgy"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_avgy()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_sxx" title="datafusion.functions.regr_sxx"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_sxx()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_syy" title="datafusion.functions.regr_syy"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_syy()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.regr_slope" title="datafusion.functions.regr_slope"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.regr_slope()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>Positional Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.first_value" title="datafusion.functions.first_value"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.first_value()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.last_value" title="datafusion.functions.last_value"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.last_value()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.nth_value" title="datafusion.functions.nth_value"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.nth_value()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>String Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.string_agg" title="datafusion.functions.string_agg"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.string_agg()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple"> |
| <dt>Approximation Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.approx_distinct" title="datafusion.functions.approx_distinct"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.approx_distinct()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.approx_median" title="datafusion.functions.approx_median"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.approx_median()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.approx_percentile_cont" title="datafusion.functions.approx_percentile_cont"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.approx_percentile_cont()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.approx_percentile_cont_with_weight" title="datafusion.functions.approx_percentile_cont_with_weight"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.approx_percentile_cont_with_weight()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| </ol> |
| </section> |
| </section> |
| |
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