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Pandas offers two methods for data extraction: loc (label-based) and iloc (position-based). loc uses row/column labels and includes the end value in slicing, making it intuitive for labeled datasets. iloc uses integer positions (starting at 0) and excludes the end value in slicing, similar to Python lists. Use loc for readable code with meaningful labels and Boolean filtering. Use iloc when working with position-based operations, unlabeled data, or when labels are messy. The article demonstrates both methods through practical examples including single/multiple row extraction, slicing, column selection, and filtering.

The Rule Everyone Misses: How to Stop Confusing loc and iloc in Pandas

Extracting a single row from the DataFrame

Extract Multiple Rows (Specific Students)

Boolean Filtering (Who scored above 80 in Math?)