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| <section id="expressions"> |
| <span id="id1"></span><h1>Expressions<a class="headerlink" href="#expressions" title="Link to this heading">¶</a></h1> |
| <p>In DataFusion an expression is an abstraction that represents a computation. |
| Expressions are used as the primary inputs and outputs for most functions within |
| DataFusion. As such, expressions can be combined to create expression trees, a |
| concept shared across most compilers and databases.</p> |
| <section id="column"> |
| <h2>Column<a class="headerlink" href="#column" title="Link to this heading">¶</a></h2> |
| <p>The first expression most new users will interact with is the Column, which is created by calling <a class="reference internal" href="../../autoapi/datafusion/index.html#datafusion.col" title="datafusion.col"><code class="xref py py-func docutils literal notranslate"><span class="pre">col()</span></code></a>. |
| This expression represents a column within a DataFrame. The function <a class="reference internal" href="../../autoapi/datafusion/index.html#datafusion.col" title="datafusion.col"><code class="xref py py-func docutils literal notranslate"><span class="pre">col()</span></code></a> takes as in input a string |
| and returns an expression as it’s output.</p> |
| </section> |
| <section id="literal"> |
| <h2>Literal<a class="headerlink" href="#literal" title="Link to this heading">¶</a></h2> |
| <p>Literal expressions represent a single value. These are helpful in a wide range of operations where |
| a specific, known value is of interest. You can create a literal expression using the function <a class="reference internal" href="../../autoapi/datafusion/index.html#datafusion.lit" title="datafusion.lit"><code class="xref py py-func docutils literal notranslate"><span class="pre">lit()</span></code></a>. |
| The type of the object passed to the <a class="reference internal" href="../../autoapi/datafusion/index.html#datafusion.lit" title="datafusion.lit"><code class="xref py py-func docutils literal notranslate"><span class="pre">lit()</span></code></a> function will be used to convert it to a known data type.</p> |
| <p>In the following example we create expressions for the column named <cite>color</cite> and the literal scalar string <cite>red</cite>. |
| The resultant variable <cite>red_units</cite> is itself also an expression.</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="n">red_units</span> <span class="o">=</span> <span class="n">col</span><span class="p">(</span><span class="s2">"color"</span><span class="p">)</span> <span class="o">==</span> <span class="n">lit</span><span class="p">(</span><span class="s2">"red"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="boolean"> |
| <h2>Boolean<a class="headerlink" href="#boolean" title="Link to this heading">¶</a></h2> |
| <p>When combining expressions that evaluate to a boolean value, you can combine these expressions using boolean operators. |
| It is important to note that in order to combine these expressions, you <em>must</em> use bitwise operators. See the following |
| examples for the and, or, and not operations.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">2</span><span class="p">]:</span> <span class="n">red_or_green_units</span> <span class="o">=</span> <span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"color"</span><span class="p">)</span> <span class="o">==</span> <span class="n">lit</span><span class="p">(</span><span class="s2">"red"</span><span class="p">))</span> <span class="o">|</span> <span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"color"</span><span class="p">)</span> <span class="o">==</span> <span class="n">lit</span><span class="p">(</span><span class="s2">"green"</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">heavy_red_units</span> <span class="o">=</span> <span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"color"</span><span class="p">)</span> <span class="o">==</span> <span class="n">lit</span><span class="p">(</span><span class="s2">"red"</span><span class="p">))</span> <span class="o">&</span> <span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"weight"</span><span class="p">)</span> <span class="o">></span> <span class="n">lit</span><span class="p">(</span><span class="mi">42</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">not_red_units</span> <span class="o">=</span> <span class="o">~</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">"color"</span><span class="p">)</span> <span class="o">==</span> <span class="n">lit</span><span class="p">(</span><span class="s2">"red"</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="arrays"> |
| <h2>Arrays<a class="headerlink" href="#arrays" title="Link to this heading">¶</a></h2> |
| <p>For columns that contain arrays of values, you can access individual elements of the array by index |
| using bracket indexing. This is similar to calling the function |
| <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.array_element" title="datafusion.functions.array_element"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.array_element()</span></code></a>, except that array indexing using brackets is 0 based, |
| similar to Python arrays and <code class="docutils literal notranslate"><span class="pre">array_element</span></code> is 1 based indexing to be compatible with other SQL |
| approaches.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">5</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="n">In</span> <span class="p">[</span><span class="mi">6</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">7</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">from_pydict</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]})</span> |
| |
| <span class="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">select</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"a0"</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">a0</span> <span class="o">|</span> |
| <span class="o">+----+</span> |
| <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="mi">4</span> <span class="o">|</span> |
| <span class="o">+----+</span> |
| </pre></div> |
| </div> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>Indexing an element of an array via <code class="docutils literal notranslate"><span class="pre">[]</span></code> starts at index 0 whereas |
| <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.array_element" title="datafusion.functions.array_element"><code class="xref py py-func docutils literal notranslate"><span class="pre">array_element()</span></code></a> starts at index 1.</p> |
| </div> |
| <p>Starting in DataFusion 49.0.0 you can also create slices of array elements using |
| slice syntax from Python.</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">select</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="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"second_two_elements"</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">second_two_elements</span> <span class="o">|</span> |
| <span class="o">+---------------------+</span> |
| <span class="o">|</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">+---------------------+</span> |
| </pre></div> |
| </div> |
| <p>To check if an array is empty, you can use the function <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.array_empty" title="datafusion.functions.array_empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.array_empty()</span></code></a> or <cite>datafusion.functions.empty</cite>. |
| This function returns a boolean indicating whether the array is empty.</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="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="n">In</span> <span class="p">[</span><span class="mi">11</span><span class="p">]:</span> <span class="kn">from</span><span class="w"> </span><span class="nn">datafusion.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">array_empty</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">12</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">13</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">from_pydict</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[[],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]})</span> |
| |
| <span class="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">select</span><span class="p">(</span><span class="n">array_empty</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">alias</span><span class="p">(</span><span class="s2">"is_empty"</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">is_empty</span> <span class="o">|</span> |
| <span class="o">+----------+</span> |
| <span class="o">|</span> <span class="n">true</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="n">false</span> <span class="o">|</span> |
| <span class="o">+----------+</span> |
| </pre></div> |
| </div> |
| <p>In this example, the <cite>is_empty</cite> column will contain <cite>True</cite> for the first row and <cite>False</cite> for the second row.</p> |
| <p>To get the total number of elements in an array, you can use the function <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.cardinality" title="datafusion.functions.cardinality"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.cardinality()</span></code></a>. |
| This function returns an integer indicating the total number of elements in the array.</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="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="n">In</span> <span class="p">[</span><span class="mi">16</span><span class="p">]:</span> <span class="kn">from</span><span class="w"> </span><span class="nn">datafusion.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">cardinality</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">17</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">18</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">from_pydict</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]})</span> |
| |
| <span class="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">select</span><span class="p">(</span><span class="n">cardinality</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">alias</span><span class="p">(</span><span class="s2">"num_elements"</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">num_elements</span> <span class="o">|</span> |
| <span class="o">+--------------+</span> |
| <span class="o">|</span> <span class="mi">3</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="mi">3</span> <span class="o">|</span> |
| <span class="o">+--------------+</span> |
| </pre></div> |
| </div> |
| <p>In this example, the <cite>num_elements</cite> column will contain <cite>3</cite> for both rows.</p> |
| <p>To concatenate two arrays, you can use the function <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.array_cat" title="datafusion.functions.array_cat"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.array_cat()</span></code></a> or <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.array_concat" title="datafusion.functions.array_concat"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.array_concat()</span></code></a>. |
| These functions return a new array that is the concatenation of the input arrays.</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="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="n">In</span> <span class="p">[</span><span class="mi">21</span><span class="p">]:</span> <span class="kn">from</span><span class="w"> </span><span class="nn">datafusion.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">array_cat</span><span class="p">,</span> <span class="n">array_concat</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">22</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">23</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">from_pydict</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span> <span class="s2">"b"</span><span class="p">:</span> <span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]})</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">24</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">array_cat</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">col</span><span class="p">(</span><span class="s2">"b"</span><span class="p">))</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"concatenated_array"</span><span class="p">))</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">24</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">concatenated_array</span> <span class="o">|</span> |
| <span class="o">+--------------------+</span> |
| <span class="o">|</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span> <span class="o">|</span> |
| <span class="o">+--------------------+</span> |
| </pre></div> |
| </div> |
| <p>In this example, the <cite>concatenated_array</cite> column will contain <cite>[1, 2, 3, 4, 5, 6]</cite>.</p> |
| <p>To repeat the elements of an array a specified number of times, you can use the function <a class="reference internal" href="../../autoapi/datafusion/functions/index.html#datafusion.functions.array_repeat" title="datafusion.functions.array_repeat"><code class="xref py py-func docutils literal notranslate"><span class="pre">datafusion.functions.array_repeat()</span></code></a>. |
| This function returns a new array with the elements repeated.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">25</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">literal</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">26</span><span class="p">]:</span> <span class="kn">from</span><span class="w"> </span><span class="nn">datafusion.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">array_repeat</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">27</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">28</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">from_pydict</span><span class="p">({</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]})</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">29</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">array_repeat</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">literal</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"repeated_array"</span><span class="p">))</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">29</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">repeated_array</span> <span class="o">|</span> |
| <span class="o">+------------------------+</span> |
| <span class="o">|</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span> <span class="o">|</span> |
| <span class="o">+------------------------+</span> |
| </pre></div> |
| </div> |
| <p>In this example, the <cite>repeated_array</cite> column will contain <cite>[[1, 2, 3], [1, 2, 3]]</cite>.</p> |
| </section> |
| <section id="structs"> |
| <h2>Structs<a class="headerlink" href="#structs" title="Link to this heading">¶</a></h2> |
| <p>Columns that contain struct elements can be accessed using the bracket notation as if they were |
| Python dictionary style objects. This expects a string key as the parameter passed.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">30</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">31</span><span class="p">]:</span> <span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"a"</span><span class="p">:</span> <span class="p">[{</span><span class="s2">"size"</span><span class="p">:</span> <span class="mi">15</span><span class="p">,</span> <span class="s2">"color"</span><span class="p">:</span> <span class="s2">"green"</span><span class="p">},</span> <span class="p">{</span><span class="s2">"size"</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">"color"</span><span class="p">:</span> <span class="s2">"blue"</span><span class="p">}]}</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">32</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">from_pydict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">33</span><span class="p">]:</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="s2">"a"</span><span class="p">)[</span><span class="s2">"size"</span><span class="p">]</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"a_size"</span><span class="p">))</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">33</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">a_size</span> <span class="o">|</span> |
| <span class="o">+--------+</span> |
| <span class="o">|</span> <span class="mi">15</span> <span class="o">|</span> |
| <span class="o">|</span> <span class="mi">10</span> <span class="o">|</span> |
| <span class="o">+--------+</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="functions"> |
| <h2>Functions<a class="headerlink" href="#functions" title="Link to this heading">¶</a></h2> |
| <p>As mentioned before, most functions in DataFusion return an expression at their output. This allows us to create |
| a wide variety of expressions built up from other expressions. For example, <a class="reference internal" href="../../autoapi/datafusion/expr/index.html#datafusion.expr.Expr.alias" title="datafusion.expr.Expr.alias"><code class="xref py py-func docutils literal notranslate"><span class="pre">alias()</span></code></a> is a function that takes |
| as it input a single expression and returns an expression in which the name of the expression has changed.</p> |
| <p>The following example shows a series of expressions that are built up from functions operating on expressions.</p> |
| <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">34</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="n">In</span> <span class="p">[</span><span class="mi">35</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">column</span><span class="p">,</span> <span class="n">lit</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">36</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">functions</span> <span class="k">as</span> <span class="n">f</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">37</span><span class="p">]:</span> <span class="kn">import</span><span class="w"> </span><span class="nn">random</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">38</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">39</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">from_pydict</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="s2">"name"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Albert"</span><span class="p">,</span> <span class="s2">"Becca"</span><span class="p">,</span> <span class="s2">"Carlos"</span><span class="p">,</span> <span class="s2">"Dante"</span><span class="p">],</span> |
| <span class="o">....</span><span class="p">:</span> <span class="s2">"age"</span><span class="p">:</span> <span class="p">[</span><span class="mi">42</span><span class="p">,</span> <span class="mi">67</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">71</span><span class="p">],</span> |
| <span class="o">....</span><span class="p">:</span> <span class="s2">"years_in_position"</span><span class="p">:</span> <span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">54</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">name</span><span class="o">=</span><span class="s2">"employees"</span> |
| <span class="o">....</span><span class="p">:</span> <span class="p">)</span> |
| <span class="o">....</span><span class="p">:</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">40</span><span class="p">]:</span> <span class="n">age_col</span> <span class="o">=</span> <span class="n">col</span><span class="p">(</span><span class="s2">"age"</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">41</span><span class="p">]:</span> <span class="n">renamed_age</span> <span class="o">=</span> <span class="n">age_col</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">"age_in_years"</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">42</span><span class="p">]:</span> <span class="n">start_age</span> <span class="o">=</span> <span class="n">age_col</span> <span class="o">-</span> <span class="n">col</span><span class="p">(</span><span class="s2">"years_in_position"</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">43</span><span class="p">]:</span> <span class="n">started_young</span> <span class="o">=</span> <span class="n">start_age</span> <span class="o"><</span> <span class="n">lit</span><span class="p">(</span><span class="mi">18</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">44</span><span class="p">]:</span> <span class="n">can_retire</span> <span class="o">=</span> <span class="n">age_col</span> <span class="o">></span> <span class="n">lit</span><span class="p">(</span><span class="mi">65</span><span class="p">)</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">45</span><span class="p">]:</span> <span class="n">long_timer</span> <span class="o">=</span> <span class="n">started_young</span> <span class="o">&</span> <span class="n">can_retire</span> |
| |
| <span class="n">In</span> <span class="p">[</span><span class="mi">46</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">long_timer</span><span class="p">)</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="s2">"name"</span><span class="p">),</span> <span class="n">renamed_age</span><span class="p">,</span> <span class="n">col</span><span class="p">(</span><span class="s2">"years_in_position"</span><span class="p">))</span> |
| <span class="n">Out</span><span class="p">[</span><span class="mi">46</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">name</span> <span class="o">|</span> <span class="n">age_in_years</span> <span class="o">|</span> <span class="n">years_in_position</span> <span class="o">|</span> |
| <span class="o">+-------+--------------+-------------------+</span> |
| <span class="o">|</span> <span class="n">Dante</span> <span class="o">|</span> <span class="mi">71</span> <span class="o">|</span> <span class="mi">54</span> <span class="o">|</span> |
| <span class="o">+-------+--------------+-------------------+</span> |
| </pre></div> |
| </div> |
| </section> |
| </section> |
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