Polars Tutorial 10 Handling Null Values скачать в хорошем качестве

Polars Tutorial 10 Handling Null Values

Polars Tutorial (Python) – Handling Null Values & NaNs 🧹 Welcome back to the Polars for Python tutorial series! In this lesson, we focus on handling null values and NaNs in Polars—one of the most important steps in real-world data cleaning and preprocessing. You’ll learn how to detect, count, drop, and fill null values using Polars’ fast, expressive, and fully vectorized API. We’ll also clarify the difference between null vs NaN, and show practical strategies for cleaning missing data in both eager and lazy workflows. Built on Apache Arrow and powered by a Rust execution engine, Polars makes missing-data operations extremely fast and memory-efficient—perfect for large datasets and production pipelines. 🔹 Code for This Video 📂 GitHub Repo: https://github.com/hthomas229/PurpleC... All notebooks and example datasets used in this tutorial are available in the repo so you can follow along step by step. 🔹 Why Polars for Missing Data? Columnar memory model (Apache Arrow) Lazy execution with query optimization Fast null detection and aggregation Clear distinction between null and NaN Vectorized drop_nulls, fill_null, and fill_nan operations 🔹 Topics Covered / Timeline 0:00 – Intro & topics covered 0:50 – Setting up a DataFrame with nulls 1:24 – Counting null values per column 6:13 – Dropping nulls (drop_nulls) 9:00 – Filling nulls (fill_null) with strategies and literals 14:40 – More on NaNs vs nulls and how to handle both 🔹 Key Polars Methods for Null Handling null_count() is_null() / is_not_null() drop_nulls() fill_null() (with values, forward fill, backward fill) fill_nan() for floating-point NaNs By the end of this tutorial, you’ll be able to clean missing data efficiently in Polars, choose the right strategy for null vs NaN, and keep your pipelines fully vectorized and production-ready. #polars #python #datacleaning #dataengineering #missingdata #apachearrow