Real-world data often requires cleaning and reshaping before it can be effectively analyzed. Having learned how to create, load, and select data using Pandas Series and DataFrames, we now turn to the essential task of data manipulation. Raw datasets frequently contain missing entries, irrelevant information, or inconvenient column names that need addressing.

This chapter introduces fundamental techniques for tidying your data within Pandas DataFrames. You will learn how to:

• Identify missing data points (often represented as $NaN$).
• Apply strategies for handling these missing values, either by removing them or filling them with appropriate substitutes.
• Add new columns, often derived from calculations on existing columns.
• Remove unwanted rows or columns.
• Rename columns for better clarity or consistency.
• Sort your DataFrame based on its index or specific column values to better organize the information.

Mastering these operations provides a solid foundation for preparing datasets for subsequent analysis or modeling tasks.