Python Polars Tutorial

Polars Tutorial python for data science

Jupyter Notebook Link : https://github.com/deepchanddc22/Polars

Unlock the power of Polars, the fast and efficient DataFrame library designed for performance and scalability. In this tutorial, we explore essential functions, data manipulation techniques, and how to leverage Polars for handling large datasets with ease. Perfect for data professionals looking to speed up their workflows without sacrificing functionality.


A short comparison of Polars vs. Pandas:

Polars:

Speed: Optimized for multi-threaded performance, much faster for large datasets.
Memory Efficiency: Uses Arrow format, making it more memory efficient.
Lazy Execution: Allows deferred operations for performance optimization.
Scalability: Handles large datasets better, especially beyond RAM size.
Pandas:

Maturity: Widely used and well-documented, with a vast ecosystem.
Ease of Use: Simple, familiar API with a broader range of features.
Single-threaded: Slower for large datasets due to lack of multi-threading.
Memory Limitations: Struggles with very large datasets.
In summary, Polars is ideal for speed and scalability, while Pandas excels in ease of use and its mature ecosystem.


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