Update 01_why_polars.py

polars/01_why_polars.py

@@ -57,7 +57,7 @@ def _(mo):
         """
         Unlike Python's earliest DataFrame library Pandas, Polars was designed with performance and usability in mind — Polars can scale to large datasets with ease while maintaining a simple and intuitive API. 
 
-        Polars' performance is due to a number of factors, including its implementation and rust and its ability to perform operations in a parallelized and vectorized manner. It supports a wide range of data types, advanced query optimizations, and seamless integration with other Python libraries, making it a versatile tool for data scientists, engineers, and analysts. Additionally, Polars provides a lazy API for deferred execution, allowing users to optimize their workflows by chaining operations and executing them in a single pass.
+        Polars' performance is due to a number of factors, including its implementation in rust and its ability to perform operations in a parallelized and vectorized manner. It supports a wide range of data types, advanced query optimizations, and seamless integration with other Python libraries, making it a versatile tool for data scientists, engineers, and analysts. Additionally, Polars provides a lazy API for deferred execution, allowing users to optimize their workflows by chaining operations and executing them in a single pass.
 
         With its focus on speed, scalability, and ease of use, Polars is quickly becoming a go-to choice for data professionals looking to streamline their data processing pipelines and tackle large-scale data challenges.
         """