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Koushik Khan

akshayka commited on Feb 10

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15a2dfa

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1 Parent(s): f2c1d1b

updated section header - Query optimization

Co-authored-by: Akshay Agrawal <[email protected]>

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polars/01_why_polars.py CHANGED Viewed

@@ -152,7 +152,7 @@ def _(mo):
 def _(mo):
     mo.md(
         """
-        ## (c) Query Optimization 📈
         A key factor behind Polars' performance lies in its **evaluation strategy**. While Pandas defaults to **eager execution**, executing operations in the exact order they are written, Polars offers both **eager and lazy execution**. With lazy execution, Polars employs a **query optimizer** that analyzes all required operations and determines the most efficient way to execute them. This optimization can involve reordering operations, eliminating redundant calculations, and more.

 def _(mo):
     mo.md(
         """
+        ### Query optimization 📈
         A key factor behind Polars' performance lies in its **evaluation strategy**. While Pandas defaults to **eager execution**, executing operations in the exact order they are written, Polars offers both **eager and lazy execution**. With lazy execution, Polars employs a **query optimizer** that analyzes all required operations and determines the most efficient way to execute them. This optimization can involve reordering operations, eliminating redundant calculations, and more.