Real Python: Working 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 video course 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 video course, you’ll understand that:
- Polarsallows you to handle missing data usingLazyFramesandDataFrames.
- You cancheck for null valuesin Polars using the
.null_count()method. - NaNrepresents non-numeric values whilenullindicates missing data.
- You canreplace NaNin Polars by converting them to nulls and using
.fill_null(). - You canfix missing databy identifying, replacing, or removing null values.
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