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| <section id="joins"> |
| <h1>Joins<a class="headerlink" href="#joins" title="Link to this heading">#</a></h1> |
| <p>DataFusion supports the following join variants via the method <a class="reference internal" href="../../autoapi/datafusion/dataframe/index.html#datafusion.dataframe.DataFrame.join" title="datafusion.dataframe.DataFrame.join"><code class="xref py py-func docutils literal notranslate"><span class="pre">join()</span></code></a></p> |
| <ul class="simple"> |
| <li><p>Inner Join</p></li> |
| <li><p>Left Join</p></li> |
| <li><p>Right Join</p></li> |
| <li><p>Full Join</p></li> |
| <li><p>Left Semi Join</p></li> |
| <li><p>Left Anti Join</p></li> |
| </ul> |
| <p>For the examples in this section we’ll use the following two DataFrames</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="n">ctx</span> <span class="o">=</span> <span class="n">SessionContext</span><span class="p">()</span> |
| |
| <span class="n">left</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="p">{</span> |
| <span class="s2">"customer_id"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="s2">"customer"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Alice"</span><span class="p">,</span> <span class="s2">"Bob"</span><span class="p">,</span> <span class="s2">"Charlie"</span><span class="p">],</span> |
| <span class="p">}</span> |
| <span class="p">)</span> |
| |
| <span class="n">right</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">([</span> |
| <span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">:</span> <span class="s2">"CityCabs"</span><span class="p">},</span> |
| <span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">:</span> <span class="s2">"MetroRide"</span><span class="p">},</span> |
| <span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">:</span> <span class="s2">"UrbanGo"</span><span class="p">},</span> |
| <span class="p">])</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <section id="inner-join"> |
| <h2>Inner Join<a class="headerlink" href="#inner-join" title="Link to this heading">#</a></h2> |
| <p>When using an inner join, only rows containing the common values between the two join columns present in both DataFrames |
| will be included in the resulting 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">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s2">"customer_id"</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s2">"id"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"inner"</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() |
| +-------------+----------+----+-----------+ |
| | customer_id | customer | id | name | |
| +-------------+----------+----+-----------+ |
| | 1 | Alice | 1 | CityCabs | |
| | 2 | Bob | 2 | MetroRide | |
| +-------------+----------+----+-----------+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <p>The parameter <code class="docutils literal notranslate"><span class="pre">join_keys</span></code> specifies the columns from the left DataFrame and right DataFrame that contains the values |
| that should match.</p> |
| </section> |
| <section id="left-join"> |
| <h2>Left Join<a class="headerlink" href="#left-join" title="Link to this heading">#</a></h2> |
| <p>A left join combines rows from two DataFrames using the key columns. It returns all rows from the left DataFrame and |
| matching rows from the right DataFrame. If there’s no match in the right DataFrame, it returns null |
| values for the corresponding columns.</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">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s2">"customer_id"</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s2">"id"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"left"</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() |
| +-------------+----------+----+-----------+ |
| | customer_id | customer | id | name | |
| +-------------+----------+----+-----------+ |
| | 1 | Alice | 1 | CityCabs | |
| | 2 | Bob | 2 | MetroRide | |
| | 3 | Charlie | | | |
| +-------------+----------+----+-----------+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="full-join"> |
| <h2>Full Join<a class="headerlink" href="#full-join" title="Link to this heading">#</a></h2> |
| <p>A full join merges rows from two tables based on a related column, returning all rows from both tables, even if there |
| is no match. Unmatched rows will have null values.</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">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s2">"customer_id"</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s2">"id"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"full"</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() |
| +-------------+----------+----+-----------+ |
| | customer_id | customer | id | name | |
| +-------------+----------+----+-----------+ |
| | 1 | Alice | 1 | CityCabs | |
| | 2 | Bob | 2 | MetroRide | |
| | | | 5 | UrbanGo | |
| | 3 | Charlie | | | |
| +-------------+----------+----+-----------+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="left-semi-join"> |
| <h2>Left Semi Join<a class="headerlink" href="#left-semi-join" title="Link to this heading">#</a></h2> |
| <p>A left semi join retrieves matching rows from the left table while |
| omitting duplicates with multiple matches in the right table.</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">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s2">"customer_id"</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s2">"id"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"semi"</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() |
| +-------------+----------+ |
| | customer_id | customer | |
| +-------------+----------+ |
| | 1 | Alice | |
| | 2 | Bob | |
| +-------------+----------+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="left-anti-join"> |
| <h2>Left Anti Join<a class="headerlink" href="#left-anti-join" title="Link to this heading">#</a></h2> |
| <p>A left anti join shows all rows from the left table without any matching rows in the right table, |
| based on a the specified matching columns. It excludes rows from the left table that have at least one matching row in |
| the right table.</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">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s2">"customer_id"</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s2">"id"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"anti"</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() |
| +-------------+----------+ |
| | customer_id | customer | |
| +-------------+----------+ |
| | 3 | Charlie | |
| +-------------+----------+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="duplicate-keys"> |
| <h2>Duplicate Keys<a class="headerlink" href="#duplicate-keys" title="Link to this heading">#</a></h2> |
| <p>It is common to join two DataFrames on a common column name. Starting in |
| version 51.0.0, <code class="docutils literal notranslate"><span class="pre">datafusion-python`</span></code> will now coalesce on column with identical names by |
| default. This reduces problems with ambiguous column selection after joins. |
| You can disable this feature by setting the parameter <code class="docutils literal notranslate"><span class="pre">coalesce_duplicate_keys</span></code> |
| to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</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">left</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="p">{</span> |
| <span class="s2">"id"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="s2">"customer"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Alice"</span><span class="p">,</span> <span class="s2">"Bob"</span><span class="p">,</span> <span class="s2">"Charlie"</span><span class="p">],</span> |
| <span class="p">}</span> |
| <span class="p">)</span> |
| |
| <span class="n">right</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">([</span> |
| <span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">:</span> <span class="s2">"CityCabs"</span><span class="p">},</span> |
| <span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">:</span> <span class="s2">"MetroRide"</span><span class="p">},</span> |
| <span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">:</span> <span class="s2">"UrbanGo"</span><span class="p">},</span> |
| <span class="p">])</span> |
| |
| <span class="n">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="s2">"id"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"inner"</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() |
| +----+----------+-----------+ |
| | id | customer | name | |
| +----+----------+-----------+ |
| | 1 | Alice | CityCabs | |
| | 2 | Bob | MetroRide | |
| +----+----------+-----------+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <p>In contrast to the above example, if we wish to get both columns:</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">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="s2">"id"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"inner"</span><span class="p">,</span> <span class="n">coalesce_duplicate_keys</span><span class="o">=</span><span class="kc">False</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() |
| +----+----------+----+-----------+ |
| | id | customer | id | name | |
| +----+----------+----+-----------+ |
| | 1 | Alice | 1 | CityCabs | |
| | 2 | Bob | 2 | MetroRide | |
| +----+----------+----+-----------+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| <section id="disambiguating-columns-with-dataframe-col"> |
| <h2>Disambiguating Columns with <code class="docutils literal notranslate"><span class="pre">DataFrame.col()</span></code><a class="headerlink" href="#disambiguating-columns-with-dataframe-col" title="Link to this heading">#</a></h2> |
| <p>When both DataFrames contain non-key columns with the same name, you can use |
| <a class="reference internal" href="../../autoapi/datafusion/dataframe/index.html#datafusion.dataframe.DataFrame.col" title="datafusion.dataframe.DataFrame.col"><code class="xref py py-meth docutils literal notranslate"><span class="pre">col()</span></code></a> on each DataFrame <strong>before</strong> the |
| join to create fully qualified column references. These references can then be |
| used in the join predicate and when selecting from the result.</p> |
| <p>This is especially useful with <a class="reference internal" href="../../autoapi/datafusion/dataframe/index.html#datafusion.dataframe.DataFrame.join_on" title="datafusion.dataframe.DataFrame.join_on"><code class="xref py py-meth docutils literal notranslate"><span class="pre">join_on()</span></code></a>, |
| which accepts expression-based predicates.</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">left</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="p">{</span> |
| <span class="s2">"id"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="s2">"val"</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">],</span> |
| <span class="p">}</span> |
| <span class="p">)</span> |
| |
| <span class="n">right</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="p">{</span> |
| <span class="s2">"id"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="s2">"val"</span><span class="p">:</span> <span class="p">[</span><span class="mi">40</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">60</span><span class="p">],</span> |
| <span class="p">}</span> |
| <span class="p">)</span> |
| |
| <span class="n">joined</span> <span class="o">=</span> <span class="n">left</span><span class="o">.</span><span class="n">join_on</span><span class="p">(</span> |
| <span class="n">right</span><span class="p">,</span> <span class="n">left</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">"id"</span><span class="p">)</span> <span class="o">==</span> <span class="n">right</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">"id"</span><span class="p">),</span> <span class="n">how</span><span class="o">=</span><span class="s2">"inner"</span> |
| <span class="p">)</span> |
| |
| <span class="n">joined</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">left</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">"id"</span><span class="p">),</span> <span class="n">left</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">"val"</span><span class="p">),</span> <span class="n">right</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">"val"</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() |
| +----+-----+-----+ |
| | id | val | val | |
| +----+-----+-----+ |
| | 1 | 10 | 40 | |
| | 2 | 20 | 50 | |
| | 3 | 30 | 60 | |
| +----+-----+-----+ |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </section> |
| </section> |
| |
| |
| </article> |
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| <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#inner-join">Inner Join</a></li> |
| <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#left-join">Left Join</a></li> |
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