Loc vs. Iloc in Pandas: When Should I Use Each for Slicing?

Loc vs. Iloc Slicing in Pandas

Loc and iloc are two commonly used slicing methods in Pandas, which provide flexibility in selecting rows and columns from a DataFrame. However, understanding their subtle differences can be confusing.

Key Distinction: Labels vs. Locations

The primary difference between loc and iloc lies in the type of indexing they employ:

• Loc: Operates on labels, specifically the actual values in the index or column labels.
• Iloc: Operates on integer locations, representing the position of rows or columns in the DataFrame.

Examples:

Consider a DataFrame with a non-monotonic integer index:

df = pd.DataFrame({
    'a': [1, 2, 3],
    'b': [4, 5, 6],
    'c': [7, 8, 9]
}, index=[0, 2, 4])

Loc:

• df.loc[0] accesses the row with index label 0, regardless of its location.
• df.loc[0:1] retrieves the rows with index labels 0 and 1.

Iloc:

• df.iloc[0] retrieves the row at index location 0, regardless of its index label.
• df.iloc[0:1] accesses only the first row since it specifies index locations as integers.

Key Differences in Usage:

Feature	Loc	Iloc
Indexing	Labels	Integer locations
Slicing	Inclusive (by default)	Exclusive (by default)
Out-of-bounds behavior	KeyError	IndexError
Negative indexing	Supported	Supported for final row only
Boolean masking	NotImplementedError	Supports boolean mask
Callable indexing	Function applied to index	Function applied to row or column

When to Use Loc vs. Iloc:

• Use loc when you need to index based on labels, such as specific names or categories.
• Prefer iloc for integer-based indexing, particularly for slicing operations with clear start and end points.
• Avoid iloc for boolean masking operations or indexing based on logical conditions.

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