Handling Missing Data with Polars

Missing data is a common challenge in data analysis, often leading to inaccurate insights if not handled correctly. This course equips you with the essential skills to confidently clean and prepare your datasets by effectively addressing missing values using the powerful Polars library. By the end of this course, you will be able to systematically approach data cleaning tasks involving missing information. What you'll learn: * Understand the different types of missing values and their representations in Polars * Identify missing data using various Polars expressions and methods * Apply strategies to fill or impute missing values based on data context * Implement techniques to drop rows or columns containing missing data * Design robust data cleaning workflows for handling missing values systematically * Evaluate the impact of missing data strategies on data types and schema This course begins with fundamental concepts of missing data, then progresses through practical techniques and best practices for managing them within Polars. You will learn to apply these methods to real-world scenarios through clear explanations and code examples. This course is designed for beginners in data analysis or anyone new to Polars, with no prior experience in handling missing data required. Start building cleaner, more reliable datasets today.