When to Use loc, iloc, at, and iat in Pandas: A Guide to Cell Localization?

Understanding Localization Methods in Pandas: loc, iloc, at, and iat

As you venture into Python from R, you may encounter confusion regarding cell localization in Pandas. Despite reading the documentation, practical applications of these methods remain elusive.

When to Use loc vs. iloc, at vs. iat?

loc and iloc: Selecting Rows and Columns

• loc: Use when accessing elements based on labels (row/column names) or boolean conditions.
• iloc: Use when accessing elements based on their numerical positions.

at and iat: Retrieving Single Values

• at: Efficiently retrieve a single value based on labels.
• iat: Efficiently retrieve a single value based on numerical positions.

Practical Applications

• Accessing a Specific Row and Column: Use loc if you know the label of the row and column, or iloc if you know their positions.
• Selecting Multiple Rows and Columns: Use loc for flexible row and column selection using labels or boolean conditions. Similarly, use iloc for numerical position-based selection.
• Retrieving Single Values: Use at or iat for quick access to a specific scalar value based on labels or numerical positions, respectively.
• Vectorized Operations: While at and iat retrieve single values, loc and iloc are suitable for performing vectorized operations on multiple elements simultaneously.

Additional Considerations

• at and iat are faster than loc and iloc since they directly access elements.
• Remember that the discontinued .ix method has been replaced by loc and iloc.

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