Python - How to rename multiple column headers in a Pandas DataFrame with Dictionary?

In Pandas, you can rename multiple column headers simultaneously using the rename() method with a dictionary. The dictionary maps old column names (keys) to new column names (values).

Syntax

DataFrame.rename(columns=dictionary, inplace=True)

Where dictionary contains old_name: new_name pairs, and inplace=True modifies the original DataFrame.

Creating a Sample DataFrame

Let's start by creating a DataFrame with car data ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Mustang', 'Tesla', 'Mustang', 'Mercedes', 'Tesla', 'Audi'],
    "Cubic Capacity": [2000, 1800, 1500, 2500, 2200, 3000, 2000],
    "Reg Price": [7000, 1500, 5000, 8000, 9000, 6000, 1500],
    "Units Sold": [200, 120, 150, 120, 210, 250, 220]
})

print("Original DataFrame:")
print(dataFrame)

Original DataFrame:
       Car  Cubic Capacity  Reg Price  Units Sold
0      BMW            2000       7000         200
1  Mustang            1800       1500         120
2    Tesla            1500       5000         150
3  Mustang            2500       8000         120
4 Mercedes            2200       9000         210
5    Tesla            3000       6000         250
6     Audi            2000       1500         220

Renaming Multiple Columns

Create a dictionary with old column names as keys and new names as values, then use rename() ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Mustang', 'Tesla', 'Mustang', 'Mercedes', 'Tesla', 'Audi'],
    "Cubic Capacity": [2000, 1800, 1500, 2500, 2200, 3000, 2000],
    "Reg Price": [7000, 1500, 5000, 8000, 9000, 6000, 1500],
    "Units Sold": [200, 120, 150, 120, 210, 250, 220]
})

# Dictionary mapping old names to new names
rename_dict = {
    'Car': 'Car Name',
    'Cubic Capacity': 'CC',
    'Reg Price': 'Registration Price',
    'Units Sold': 'Units Purchased'
}

# Rename columns using the dictionary
dataFrame.rename(columns=rename_dict, inplace=True)

print("DataFrame with renamed columns:")
print(dataFrame)

DataFrame with renamed columns:
  Car Name    CC  Registration Price  Units Purchased
0      BMW  2000                7000              200
1  Mustang  1800                1500              120
2    Tesla  1500                5000              150
3  Mustang  2500                8000              120
4 Mercedes  2200                9000              210
5    Tesla  3000                6000              250
6     Audi  2000                1500              220

Alternative Approach Without inplace

You can also create a new DataFrame without modifying the original ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Mustang', 'Tesla'],
    "Price": [50000, 30000, 80000]
})

rename_dict = {'Car': 'Vehicle', 'Price': 'Cost'}

# Create new DataFrame with renamed columns
new_dataFrame = dataFrame.rename(columns=rename_dict)

print("Original DataFrame:")
print(dataFrame)
print("\nNew DataFrame:")
print(new_dataFrame)

Original DataFrame:
       Car  Price
0      BMW  50000
1  Mustang  30000
2    Tesla  80000

New DataFrame:
   Vehicle   Cost
0      BMW  50000
1  Mustang  30000
2    Tesla  80000

Conclusion

Use rename(columns=dictionary) to rename multiple columns at once. Set inplace=True to modify the original DataFrame, or omit it to create a new DataFrame with renamed columns.