Data Types
Understanding and managing data types is crucial for:
Duplicates
Duplicate rows are common in real-world data. _pandas_ provides simple methods to find and remove them.
Missing Data
Missing data appears as NaN (Not a Number), None, or NaT (Not a Time) in pandas. Handling it correctly is crucial for data analysis.
Outliers & Validation
Outliers are data points that differ significantly from other observations. Data validation ensures your data meets expected criteria before analysis.
String Operations
pandas provides string methods through the .str accessor. These methods work on Series containing strings and are essential for text data cleaning.