Pandas Indexing: A Comprehensive Guide to Loc and Iloc
Understanding Loc and Iloc
Indexing in Pandas is essential for extracting specific data from a DataFrame. Two commonly used indexing methods are loc and iloc. Loc enables label-based indexing, allowing you to access data using row and column labels. Iloc, on the other hand, provides integer position-based indexing, where you specify the row and column indices.
iloc: Integer Position-Based Indexing
Iloc (an abbreviation for "integer location") focuses on numerical indexing. It takes integer values as indices and retrieves the corresponding values. The indices start from 0 for both rows and columns, with 0 denoting the first row or column.
loc: Label-Based Indexing
Loc (short for "label-based location") allows you to index data using labels or names. You can specify row and column labels as strings or lists of strings to extract the desired data. Loc is particularly useful when working with DataFrames that have named indices.
Practical Applications
Loc and iloc are powerful tools for data manipulation in Pandas. Here are some common use cases:
- Extracting rows: Use loc or iloc to select specific rows by index or label.
- Extracting columns: Use loc or iloc to retrieve columns by name or position.
- Slicing data: Use loc or iloc in combination with slicing to extract subsets of rows and columns.
- Setting values: Assign new values to specific rows and columns using loc or iloc.
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