updated text for intro

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

polars/01_why_polars.py

@@ -29,7 +29,7 @@ def _(mo):
 
         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.
 
-        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. Whether you're analyzing gigabytes of data or performing real-time computations, Polars empowers you to work faster and smarter.
+        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.
         """
     )
     return