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| <section id="window-functions"> |
| <span id="id1"></span><h1>Window Functions<a class="headerlink" href="#window-functions" title="Link to this heading">#</a></h1> |
| <p>In this section you will learn about window functions. A window function utilizes values from one or |
| multiple rows to produce a result for each individual row, unlike an aggregate function that |
| provides a single value for multiple rows.</p> |
| <p>The window functions are available in the <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#module-datafusion.functions" title="datafusion.functions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">functions</span></code></a> module.</p> |
| <p>We’ll use the pokemon dataset (from Ritchie Vink) in the following examples.</p> |
| <div class="cell docutils container"> |
| <div class="cell_input docutils container"> |
| <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></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="kn">from</span><span class="w"> </span><span class="nn">datafusion</span><span class="w"> </span><span class="kn">import</span> <span class="n">col</span><span class="p">,</span> <span class="n">lit</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">functions</span> <span class="k">as</span> <span class="n">f</span> |
| |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</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> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <p>Here is an example that shows how you can compare each pokemon’s speed to the speed of the |
| previous row in the DataFrame.</p> |
| <div class="cell docutils container"> |
| <div class="cell_input docutils container"> |
| <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">select</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="n">col</span><span class="p">(</span><span class="s1">'"Speed"'</span><span class="p">),</span> |
| <span class="n">f</span><span class="o">.</span><span class="n">lag</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s1">'"Speed"'</span><span class="p">))</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Previous Speed"</span><span class="p">)</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="cell_output docutils container"> |
| <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DataFrame() |
| +---------------------------+-------+----------------+ |
| | Name | Speed | Previous Speed | |
| +---------------------------+-------+----------------+ |
| | Bulbasaur | 45 | | |
| | Ivysaur | 60 | 45 | |
| | Venusaur | 80 | 60 | |
| | VenusaurMega Venusaur | 80 | 80 | |
| | Charmander | 65 | 80 | |
| | Charmeleon | 80 | 65 | |
| | Charizard | 100 | 80 | |
| | CharizardMega Charizard X | 100 | 100 | |
| | CharizardMega Charizard Y | 100 | 100 | |
| | Squirtle | 43 | 100 | |
| +---------------------------+-------+----------------+ |
| Data truncated. |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <section id="setting-parameters"> |
| <h2>Setting Parameters<a class="headerlink" href="#setting-parameters" title="Link to this heading">#</a></h2> |
| <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.</p> |
| <div class="cell docutils container"> |
| <div class="cell_input docutils container"> |
| <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">select</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="n">col</span><span class="p">(</span><span class="s1">'"Attack"'</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="n">f</span><span class="o">.</span><span class="n">rank</span><span class="p">(</span> |
| <span class="n">partition_by</span><span class="o">=</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="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="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"rank"</span><span class="p">),</span> |
| <span class="p">)</span><span class="o">.</span><span class="n">sort</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="n">col</span><span class="p">(</span><span class="s1">'"Attack"'</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="cell_output docutils container"> |
| <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DataFrame() |
| +------------+--------+--------+------+ |
| | Name | Attack | Type 1 | rank | |
| +------------+--------+--------+------+ |
| | Metapod | 20 | Bug | 1 | |
| | Kakuna | 25 | Bug | 2 | |
| | Caterpie | 30 | Bug | 3 | |
| | Weedle | 35 | Bug | 4 | |
| | Butterfree | 45 | Bug | 5 | |
| | Venonat | 55 | Bug | 6 | |
| | Venomoth | 65 | Bug | 7 | |
| | Paras | 70 | Bug | 8 | |
| | Beedrill | 90 | Bug | 9 | |
| | Parasect | 95 | Bug | 10 | |
| +------------+--------+--------+------+ |
| Data truncated. |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="partitions"> |
| <h3>Partitions<a class="headerlink" href="#partitions" title="Link to this heading">#</a></h3> |
| <p>A window function can take a list of <code class="docutils literal notranslate"><span class="pre">partition_by</span></code> columns similar to an |
| <a class="reference internal" href="aggregations.html#aggregation"><span class="std std-ref">Aggregation Function</span></a>. This will cause the window values to be evaluated |
| independently for each of the partitions. In the example above, we found the rank of each |
| Pokemon per <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">1</span></code> partitions. We can see the first couple of each partition if we do |
| the following:</p> |
| <div class="cell docutils container"> |
| <div class="cell_input docutils container"> |
| <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">select</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="n">col</span><span class="p">(</span><span class="s1">'"Attack"'</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="n">f</span><span class="o">.</span><span class="n">rank</span><span class="p">(</span> |
| <span class="n">partition_by</span><span class="o">=</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="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="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"rank"</span><span class="p">),</span> |
| <span class="p">)</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"rank"</span><span class="p">)</span> <span class="o"><</span> <span class="n">lit</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">sort</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="n">col</span><span class="p">(</span><span class="s2">"rank"</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="cell_output docutils container"> |
| <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DataFrame() |
| +-----------+--------+----------+------+ |
| | Name | Attack | Type 1 | rank | |
| +-----------+--------+----------+------+ |
| | Metapod | 20 | Bug | 1 | |
| | Kakuna | 25 | Bug | 2 | |
| | Dratini | 64 | Dragon | 1 | |
| | Dragonair | 84 | Dragon | 2 | |
| | Voltorb | 30 | Electric | 1 | |
| | Magnemite | 35 | Electric | 2 | |
| | Clefairy | 45 | Fairy | 1 | |
| | Clefable | 70 | Fairy | 2 | |
| | Mankey | 80 | Fighting | 1 | |
| | Machop | 80 | Fighting | 1 | |
| +-----------+--------+----------+------+ |
| Data truncated. |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="window-frame"> |
| <h3>Window Frame<a class="headerlink" href="#window-frame" title="Link to this heading">#</a></h3> |
| <p>When using aggregate functions, the Window Frame of defines the rows over which it operates. |
| If you do not specify a Window Frame, the frame will be set depending on the following |
| criteria.</p> |
| <ul class="simple"> |
| <li><p>If an <code class="docutils literal notranslate"><span class="pre">order_by</span></code> clause is set, the default window frame is defined as the rows between |
| unbounded preceding and the current row.</p></li> |
| <li><p>If an <code class="docutils literal notranslate"><span class="pre">order_by</span></code> is not set, the default frame is defined as the rows between unbounded |
| and unbounded following (the entire partition).</p></li> |
| </ul> |
| <p>Window Frames are defined by three parameters: unit type, starting bound, and ending bound.</p> |
| <p>The unit types available are:</p> |
| <ul class="simple"> |
| <li><p>Rows: The starting and ending boundaries are defined by the number of rows relative to the |
| current row.</p></li> |
| <li><p>Range: When using Range, the <code class="docutils literal notranslate"><span class="pre">order_by</span></code> clause must have exactly one term. The boundaries |
| are defined bow how close the rows are to the value of the expression in the <code class="docutils literal notranslate"><span class="pre">order_by</span></code> |
| parameter.</p></li> |
| <li><p>Groups: A “group” is the set of all rows that have equivalent values for all terms in the |
| <code class="docutils literal notranslate"><span class="pre">order_by</span></code> clause.</p></li> |
| </ul> |
| <p>In this example we perform a “rolling average” of the speed of the current Pokemon and the |
| two preceding rows.</p> |
| <div class="cell docutils container"> |
| <div class="cell_input docutils container"> |
| <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">datafusion.expr</span><span class="w"> </span><span class="kn">import</span> <span class="n">Window</span><span class="p">,</span> <span class="n">WindowFrame</span> |
| |
| <span class="n">df</span><span class="o">.</span><span class="n">select</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="n">col</span><span class="p">(</span><span class="s1">'"Speed"'</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</span><span class="p">(</span><span class="s1">'"Speed"'</span><span class="p">))</span> |
| <span class="o">.</span><span class="n">over</span><span class="p">(</span><span class="n">Window</span><span class="p">(</span><span class="n">window_frame</span><span class="o">=</span><span class="n">WindowFrame</span><span class="p">(</span><span class="s2">"rows"</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">order_by</span><span class="o">=</span><span class="p">[</span><span class="n">col</span><span class="p">(</span><span class="s1">'"Speed"'</span><span class="p">)]))</span> |
| <span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Previous Speed"</span><span class="p">),</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="cell_output docutils container"> |
| <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DataFrame() |
| +------------+-------+--------------------+ |
| | Name | Speed | Previous Speed | |
| +------------+-------+--------------------+ |
| | Slowpoke | 15 | 15.0 | |
| | Jigglypuff | 20 | 17.5 | |
| | Geodude | 20 | 18.333333333333332 | |
| | Paras | 25 | 21.666666666666668 | |
| | Grimer | 25 | 23.333333333333332 | |
| | Rhyhorn | 25 | 25.0 | |
| | Snorlax | 30 | 26.666666666666668 | |
| | Metapod | 30 | 28.333333333333332 | |
| | Oddish | 30 | 30.0 | |
| | Parasect | 30 | 30.0 | |
| +------------+-------+--------------------+ |
| Data truncated. |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="null-treatment"> |
| <h3>Null Treatment<a class="headerlink" href="#null-treatment" title="Link to this heading">#</a></h3> |
| <p>When using aggregate functions as window functions, it is often useful to specify how null values |
| should be treated. In order to do this you need to use the builder function. In future releases |
| we expect this to be simplified in the interface.</p> |
| <p>One common usage for handling nulls is the case where you want to find the last value up to the |
| current row. In the following example we demonstrate how setting the null treatment to ignore |
| nulls will fill in with the value of the most recent non-null row. To do this, we also will set |
| the window frame so that we only process up to the current row.</p> |
| <p>In this example, we filter down to one specific type of Pokemon that does have some entries in |
| it’s <code class="docutils literal notranslate"><span class="pre">Type</span> <span class="pre">2</span></code> column that are null.</p> |
| <div class="cell docutils container"> |
| <div class="cell_input docutils container"> |
| <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></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">df</span><span class="o">.</span><span class="n">filter</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="n">lit</span><span class="p">(</span><span class="s2">"Bug"</span><span class="p">))</span><span class="o">.</span><span class="n">select</span><span class="p">(</span> |
| <span class="s1">'"Name"'</span><span class="p">,</span> |
| <span class="s1">'"Type 2"'</span><span class="p">,</span> |
| <span class="n">f</span><span class="o">.</span><span class="n">last_value</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s1">'"Type 2"'</span><span class="p">))</span> |
| <span class="o">.</span><span class="n">over</span><span class="p">(</span> |
| <span class="n">Window</span><span class="p">(</span> |
| <span class="n">window_frame</span><span class="o">=</span><span class="n">WindowFrame</span><span class="p">(</span><span class="s2">"rows"</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</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">'"Speed"'</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="p">,</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">"last_wo_null"</span><span class="p">),</span> |
| <span class="n">f</span><span class="o">.</span><span class="n">last_value</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s1">'"Type 2"'</span><span class="p">))</span> |
| <span class="o">.</span><span class="n">over</span><span class="p">(</span> |
| <span class="n">Window</span><span class="p">(</span> |
| <span class="n">window_frame</span><span class="o">=</span><span class="n">WindowFrame</span><span class="p">(</span><span class="s2">"rows"</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</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">'"Speed"'</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="p">,</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">"last_with_null"</span><span class="p">),</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="cell_output docutils container"> |
| <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DataFrame() |
| +------------+--------+--------------+----------------+ |
| | Name | Type 2 | last_wo_null | last_with_null | |
| +------------+--------+--------------+----------------+ |
| | Paras | Grass | Grass | Grass | |
| | Metapod | | Grass | | |
| | Parasect | Grass | Grass | Grass | |
| | Kakuna | Poison | Poison | Poison | |
| | Caterpie | | Poison | | |
| | Venonat | Poison | Poison | Poison | |
| | Weedle | Poison | Poison | Poison | |
| | Butterfree | Flying | Flying | Flying | |
| | Beedrill | Poison | Poison | Poison | |
| | Pinsir | | Poison | | |
| +------------+--------+--------------+----------------+ |
| Data truncated. |
| </pre></div> |
| </div> |
| </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>You can use any <a class="reference internal" href="aggregations.html#aggregation"><span class="std std-ref">Aggregation Function</span></a> as a window function. Here |
| is an example that shows how to compare each pokemons’s attack power with the average attack |
| power in its <code class="docutils literal notranslate"><span class="pre">"Type</span> <span class="pre">1"</span></code> using the <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> function.</p> |
| <div class="cell docutils container"> |
| <div class="cell_input docutils container"> |
| <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">select</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="n">col</span><span class="p">(</span><span class="s1">'"Attack"'</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="n">f</span><span class="o">.</span><span class="n">avg</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">over</span><span class="p">(</span> |
| <span class="n">Window</span><span class="p">(</span> |
| <span class="n">window_frame</span><span class="o">=</span><span class="n">WindowFrame</span><span class="p">(</span><span class="s2">"rows"</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="n">partition_by</span><span class="o">=</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="p">)</span> |
| <span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"Average Attack"</span><span class="p">),</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="cell_output docutils container"> |
| <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DataFrame() |
| +-------------------+--------+--------+----------------+ |
| | Name | Attack | Type 1 | Average Attack | |
| +-------------------+--------+--------+----------------+ |
| | Dragonite | 134 | Dragon | 94.0 | |
| | Dratini | 64 | Dragon | 94.0 | |
| | Dragonair | 84 | Dragon | 94.0 | |
| | Gastly | 35 | Ghost | 53.75 | |
| | Haunter | 50 | Ghost | 53.75 | |
| | Gengar | 65 | Ghost | 53.75 | |
| | GengarMega Gengar | 65 | Ghost | 53.75 | |
| | Jynx | 50 | Ice | 67.5 | |
| | Articuno | 85 | Ice | 67.5 | |
| | Kabuto | 80 | Rock | 87.5 | |
| +-------------------+--------+--------+----------------+ |
| Data truncated. |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="available-functions"> |
| <h2>Available Functions<a class="headerlink" href="#available-functions" title="Link to this heading">#</a></h2> |
| <p>The possible window functions are:</p> |
| <ol class="arabic simple"> |
| <li><dl class="simple myst"> |
| <dt>Rank Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.rank" title="datafusion.functions.rank"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.rank()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.dense_rank" title="datafusion.functions.dense_rank"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.dense_rank()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.ntile" title="datafusion.functions.ntile"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.ntile()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.row_number" title="datafusion.functions.row_number"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.row_number()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple myst"> |
| <dt>Analytical Functions</dt><dd><ul class="simple"> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.cume_dist" title="datafusion.functions.cume_dist"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.cume_dist()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.percent_rank" title="datafusion.functions.percent_rank"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.percent_rank()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.lag" title="datafusion.functions.lag"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.lag()</span></code></a></p></li> |
| <li><p><a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.lead" title="datafusion.functions.lead"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.lead()</span></code></a></p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| <li><dl class="simple myst"> |
| <dt>Aggregate Functions</dt><dd><ul class="simple"> |
| <li><p>All <a class="reference internal" href="aggregations.html#aggregation"><span class="std std-ref">Aggregation Functions</span></a> can be used as window functions.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </li> |
| </ol> |
| </section> |
| <section id="user-defined-window-functions"> |
| <h2>User-Defined Window Functions<a class="headerlink" href="#user-defined-window-functions" title="Link to this heading">#</a></h2> |
| <p>You can ship custom window functions to the engine by subclassing |
| <a class="reference internal" href="../../autoapi/datafusion/user_defined/index.html#datafusion.user_defined.WindowEvaluator" title="datafusion.user_defined.WindowEvaluator"><code class="xref py py-class docutils literal notranslate"><span class="pre">WindowEvaluator</span></code></a> and registering it |
| via <a class="reference internal" href="../../autoapi/datafusion/index.html#datafusion.udwf" title="datafusion.udwf"><code class="xref py py-func docutils literal notranslate"><span class="pre">udwf()</span></code></a>. See <a class="reference internal" href="../../autoapi/datafusion/user_defined/index.html#module-datafusion.user_defined" title="datafusion.user_defined"><code class="xref py py-mod docutils literal notranslate"><span class="pre">datafusion.user_defined</span></code></a> |
| for the evaluator interface and worked examples.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Serialization</p> |
| <p>Python window UDFs travel inline inside pickled or |
| <a class="reference internal" href="../../autoapi/datafusion/expr/index.html#datafusion.expr.Expr.to_bytes" title="datafusion.expr.Expr.to_bytes"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_bytes()</span></code></a>-serialized expressions — |
| the evaluator class is captured by value via <code class="xref py py-mod docutils literal notranslate"><span class="pre">cloudpickle</span></code>, so |
| worker processes do not need to pre-register the UDF. Any names the |
| evaluator resolves via <code class="docutils literal notranslate"><span class="pre">import</span></code> are captured <strong>by reference</strong> and |
| must be importable on the receiving worker. See |
| <a class="reference internal" href="../../autoapi/datafusion/ipc/index.html#module-datafusion.ipc" title="datafusion.ipc"><code class="xref py py-mod docutils literal notranslate"><span class="pre">datafusion.ipc</span></code></a> for the full IPC model and security caveats.</p> |
| </div> |
| </section> |
| </section> |
| |
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