How to Deal With Missing Data in Polars
Efficiently handling missing data in Polars is essential for keeping your datasets clean during analysis. Polars provides powerful tools to identify, replace, and remove null values, ensuring seamless data processing.
This tutorial covers practical techniques for managing missing data and highlights Polars’ capabilities to enhance your data analysis workflow. By following along, you’ll gain hands-on experience with these techniques and learn how to ensure your datasets are accurate and reliable.
By the end of this tutorial, you’ll understand that:
- Polars allows you to handle missing data using LazyFrames and DataFrames.
- You can check for null values in Polars using the
.null_count()method. - NaN represents non-numeric values while null indicates missing data.
- You can replace NaN in Polars by converting