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Author: khuyentran1401

Chapter5/SQL.html

@@ -1024,15 +1024,6 @@ <h2><span class="section-number">6.13.7. </span>Efficient SQL Operations with Du
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 <p>Using SQL with pandas empowers data scientists to leverage SQL’s powerful querying capabilities alongside the data manipulation functionalities of pandas.</p>
-<p>However, traditional database systems often demand the management of a separate DBMS server, introducing additional complexity to the workflow.</p>
-<p>With DuckDB, you can efficiently run SQL operations on pandas DataFrames without the need to manage a separate DBMS server.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">duckdb</span>
-
-<span class="n">mydf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</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="nb">print</span><span class="p">(</span><span class="n">duckdb</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">&quot;SELECT SUM(a) FROM mydf&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to_df</span><span class="p">())</span>
-</pre></div>
-</div>
 <p>In the code below, aggregating data using DuckDB is nearly 6 times faster compared to aggregating with pandas.</p>
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