This tutorial introduces Polars with practical examples. Polars supports lazy and eager execution modes, making it ideal for large datasets. It provides a Pandas-like API with additional optimizations. Creating
Note: Throughout this tutorial, you’ll work with both NumPy arrays and pandas DataFrames, so you need to make sure you have those features. If don’t want to install Polars with python -m pip install "polars[all]", then you’ll need to at least run python -m pip install "polars[...
The first function is writing a CSV file using Pandas.to_csv and Polars.write_csv. To have enough data points for statistical testing, the code below shows how we will iterate the writing function 50 times. If you follow this tutorial, please be aware that each...
What the lazy API is and how to build lazy queries Here are additional resources mentioned in the course: How to Deal With Missing Data in Polars Speeding Up Your DataFrames With Polars (The Real Python Podcast) NumPy Practical Examples: Useful Techniques tutorial course The pandas DataFrame: ...
This tutorial covers how to use Polars DataFrames with practical examples. Polars provides a DataFrame API similar to Pandas but with better performance. It is optimized for large datasets and supports lazy evaluation for efficient query execution. ...