Difference between loc() and iloc() in Pandas DataFrame

In pandas, loc[] and iloc[] are both used to access elements of a DataFrame, but they differ in how they index the rows and columns.

1. loc[] – Label-based indexing:

• loc[] is used for label-based indexing, meaning you refer to rows and columns by their labels (the index names or column names).
• Both rows and columns are accessed using their labels.
• It includes both the start and end labels when slicing.

Example:

import pandas as pd
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}, index=['row1', 'row2', 'row3'])

# Access by label
df.loc['row1', 'A']  # Returns 1 (value in row1, column 'A')
df.loc['row1':'row2', 'A']  # Slices rows 'row1' to 'row2', column 'A'

2. iloc[] – Integer-location based indexing:

• iloc[] is used for integer-location based indexing, meaning you refer to rows and columns by their integer positions (i.e., 0-based index).
• It works with positional indexing, so you use integer indices for both rows and columns.
• When slicing, it excludes the end position.

Example:

# Access by position (0-based index)
df.iloc[0, 0]  # Returns 1 (first row, first column)
df.iloc[0:2, 0]  # Slices rows 0 to 1, column 0 (does not include row 2)

Summary:

• loc[]: Accesses data using labels (index and column names), and includes the end index in slicing.
• iloc[]: Accesses data using integer positions, and excludes the end position in slicing.