Loc vs. Iloc, At vs. Iat: How Do You Choose the Right Data Extraction Method in Pandas?

Data Extraction Options in Pandas: loc, iloc, at, and iat

Understanding cell localization and selection in Pandas can be challenging, especially as a new Python user coming from R. This guide aims to clarify the practical differences between the various options: .loc, .iloc, .at, and .iat.

When to Use loc vs. iloc

• .loc: Accesses data by label (row and column indices). Ideal for working with data that has meaningful names or categories as indices.
• .iloc: Accesses data by position (integer-based). Useful for extracting specific rows or columns based on their numerical indices.

Example:

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Access value at row index 'C' and column index 'A' using .loc (label)
value1 = df.loc['C', 'A']  # Error, as 'C' is not a valid row index

# Access value at row index 2 and column index 0 using .iloc (integer)
value2 = df.iloc[2, 0]  # Returns 3

When to Use at vs. iat

• .at: Retrieves a single scalar value by label (similar to .loc).
• .iat: Retrieves a single scalar value by position (similar to .iloc).

Both .at and .iat are optimized for fast access to single values, making them more efficient than .loc or .iloc for scalar operations.

Example:

value3 = df.at['B', 'A']  # Returns 4 using label-based indexing
value4 = df.iat[1, 0]  # Returns 2 using position-based indexing

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