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

Data extraction in the Pandas library often causes confusion between the `loc` and `iloc` accessors. The primary distinction is that `loc` selects data based on explicit labels, while `iloc` uses integer-based positional indexing, similar to Python lists. Using a sample student dataset, the content provides code examples for selecting single rows, slicing ranges of rows, extracting specific columns, and performing boolean filtering. It also explains the key difference in slicing behavior, where `loc` is inclusive of the end point and `iloc` is exclusive, and discusses scenarios where each method is preferable.