SQL with Python with Polars DataFrame library (hands-on tutorial)

This video shows how to execute SQL queries with Python with Polars DataFrame library. You can use Polars for any kind of tabular data stored in CSV, Parquet, or other standard data file formats.

Polars module is built with Rust which gives it C++ performance and allows to control performance. There are two API:
- for Rust user: https://pola-rs.github.io/polars/polars/
- for Python user: https://pola-rs.github.io/polars/py-polars/html/reference/

You can use Polars as a DataFrame library or as query engine backend for your data models. Because of this reason it is beautiful choice for data scientists and data analytics who need handle big amount of data or are more familiar with SQL than Pandas (in Python).

Polars supports Numpy universal and Windows functions, also provides so popular statistics and aggregation functions such as GroupBy, Folds, and Regular Expressions. Also, you can use it with Selecting, Handling, Combining, Multiprocessing data, and even with Time Series data.

Useful links
- Polars User Guide: https://pola-rs.github.io/polars-book/user-guide/index.html
- Polars main website: https://www.pola.rs/

Content of the video:
0:00 - What is Polars DataFrame library
1:32 - Your first Python code with Polars

In some aspects, Polars can be a good alternative to Spark SQL framework.

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