polars

Explore the polars library to understand its Rust implementation, lazy evaluation, multi-threading, and out-of-core processing. Learn how to use polars for faster data manipulation and analytics, especially with large datasets, while leveraging a pandas-like DataFrame API.

We'll cover the following...

Introduction
Walkthrough
Data types in polars

Introduction

In this lesson, we’ll learn about a powerful Python library called polars. It’s a data manipulation library implemented in Rust and designed to work efficiently with large datasets, offering a fast and flexible alternative to the classic pandas library. The polars library has also been shown to demonstrate significant speed improvements on pandas in data processing efficiency benchmarks, such as for Join operations.

The polars library is able to achieve these performance improvements due to the following reasons: ...

1.Before We Begin

2.Reading Data into pandas

3.Combining Data

4.Reshaping and Manipulating Data

5.Encoding Data Types

6.Handling Numerical Data

7.Handling Categorical Data

8.Handling Text Data

9.Handling Time Series Data

10.Handling Sparse Data Structures

11.Handling Missing Data

Project

12.Leveraging Further Features of pandas

13.Utilizing Extended Libraries

14.Wrap Up

15.Appendix

Project

polars

Introduction