pandas best practices 5 10 Handling missing values

Pandas best practices, in this course we will learn about writing efficient, clean, and professional data analysis code using the powerful Python library, Pandas. You'll explore essential techniques to optimize performance, reduce memory usage, and handle data more effectively. We’ll cover best practices for data cleaning, filtering, merging, grouping, and aggregating, as well as avoiding common pitfalls and performance bottlenecks. You’ll learn how to write vectorized operations instead of slow loops, manage large datasets, and use proper indexing and selection methods. The course will also highlight good coding standards, reproducibility, and readability—key traits of high-quality data analysis workflows. Whether you’re working on personal projects, academic research, or production pipelines, this course will help you master Pandas the right way and improve your productivity as a data analyst or data scientist. Basic Pandas knowledge is recommended. Data School