Real-world datasets are often incomplete. Entries might be missing due to errors during data collection, transmission issues, or simply because the information was not available. These gaps, frequently represented as NaN (Not a Number), NULL, or other placeholders, can significantly interfere with data analysis and the performance of machine learning models. Many algorithms cannot function correctly when faced with missing values.

This chapter focuses on equipping you with fundamental methods to manage this common issue. You will learn:

• How to identify the presence, patterns, and extent of missing data within your datasets using programmatic techniques and basic visualizations.
• Different strategies for handling these gaps, including:
  • Removing rows or columns containing missing values (deletion).
  • Filling missing entries with calculated substitutes like the mean, median, or mode (imputation).
• Factors to consider when choosing an appropriate strategy for your specific situation and data.

We will use practical examples to illustrate these techniques, providing a foundation for preparing cleaner, more reliable data.