Renaming Axes¶

The rename() method changes labels of axes (columns or index). Clear, descriptive names improve code readability and data understanding.

Column Renaming¶

Rename DataFrame columns.

1. Dictionary Mapping¶

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30]
})

df.rename(columns={'Name': 'First Name'}, inplace=True)
print(df)

  First Name  Age
0      Alice   25
1        Bob   30

2. Multiple Columns¶

df.rename(columns={
    'col_a': 'Column A',
    'col_b': 'Column B',
    'col_c': 'Column C'
})

3. Function Mapping¶

df.rename(columns=str.upper)      # All uppercase
df.rename(columns=str.lower)      # All lowercase
df.rename(columns=lambda x: x.strip())  # Remove whitespace

Index Renaming¶

Rename row labels.

1. Dictionary Mapping¶

df = pd.DataFrame({
    'A': [1, 2, 3]
}, index=['row1', 'row2', 'row3'])

df.rename(index={'row1': 'first_row'})

2. Function Mapping¶

df.rename(index=str.upper)

3. Both Axes¶

df.rename(
    columns={'A': 'Column A'},
    index={'row1': 'Row 1'}
)

set_axis Method¶

Replace all labels at once.

1. Replace Columns¶

df.set_axis(['new_col1', 'new_col2'], axis=1)

2. Replace Index¶

df.set_axis(['a', 'b', 'c'], axis=0)

3. Must Match Length¶

# Number of new labels must equal existing labels

columns Property¶

Direct assignment to columns attribute.

1. Replace All Columns¶

df.columns = ['new_name1', 'new_name2']

2. List Comprehension¶

df.columns = [col.replace(' ', '_') for col in df.columns]

3. Prefix and Suffix¶

df.columns = ['prefix_' + col for col in df.columns]
df.columns = [col + '_suffix' for col in df.columns]

add_prefix and add_suffix¶

Add text to all column names.

1. Add Prefix¶

df.add_prefix('2024_')

   2024_A  2024_B
0       1       4
1       2       5

2. Add Suffix¶

df.add_suffix('_value')

3. Chaining¶

df.add_prefix('raw_').add_suffix('_v1')

Practical Examples¶

Common renaming scenarios.

1. Clean Column Names¶

def clean_column_name(name):
    return (name
        .lower()
        .strip()
        .replace(' ', '_')
        .replace('-', '_'))

df.rename(columns=clean_column_name)

2. Financial Data¶

df.rename(columns={
    'Open': 'open_price',
    'High': 'high_price',
    'Low': 'low_price',
    'Close': 'close_price',
    'Volume': 'trading_volume'
})

3. Multi-source Merge¶

# Add source identifier before merge
df1.add_suffix('_source1')
df2.add_suffix('_source2')
pd.merge(df1, df2, left_index=True, right_index=True)