Python - Add a prefix to column names in a Pandas DataFrame

A Pandas DataFrame allows you to add prefixes to all column names using the add_prefix() method. This is useful for distinguishing columns when merging DataFrames or organizing data.

Syntax

DataFrame.add_prefix(prefix)

Parameters:

• prefix ? String to add before each column name

Creating a DataFrame

First, let's create a DataFrame with car data ?

import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 120, 150, 110, 200, 250]
})

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

Original DataFrame:
        Car  Cubic_Capacity  Reg_Price  Units_Sold
0       BMW            2000       7000         100
1     Lexus            1800       1500         120
2     Tesla            1500       5000         150
3   Mustang            2500       8000         110
4  Mercedes            2200       9000         200
5    Jaguar            3000       6000         250

Adding Prefix to Column Names

Use add_prefix() to add a prefix to all column names ?

import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 120, 150, 110, 200, 250]
})

# Add prefix to column names
updated_df = dataFrame.add_prefix('column_')
print("DataFrame with prefix:")
print(updated_df)

DataFrame with prefix:
  column_Car  column_Cubic_Capacity  column_Reg_Price  column_Units_Sold
0        BMW                   2000              7000                100
1      Lexus                   1800              1500                120
2      Tesla                   1500              5000                150
3    Mustang                   2500              8000                110
4   Mercedes                   2200              9000                200
5     Jaguar                   3000              6000                250

Common Use Cases

The add_prefix() method is commonly used when ?

• Merging DataFrames to avoid column name conflicts
• Organizing columns by category or source
• Creating consistent naming conventions

import pandas as pd

# Example: Different prefixes for different datasets
sales_data = pd.DataFrame({
    "Q1": [100, 200], 
    "Q2": [150, 180]
})

budget_data = pd.DataFrame({
    "Q1": [120, 190], 
    "Q2": [160, 170]
})

# Add descriptive prefixes
sales_prefixed = sales_data.add_prefix('sales_')
budget_prefixed = budget_data.add_prefix('budget_')

print("Sales data with prefix:")
print(sales_prefixed)
print("\nBudget data with prefix:")
print(budget_prefixed)

Sales data with prefix:
   sales_Q1  sales_Q2
0       100       150
1       200       180

Budget data with prefix:
   budget_Q1  budget_Q2
0        120        160
1        190        170

Conclusion

The add_prefix() method provides a simple way to add prefixes to all DataFrame column names. It returns a new DataFrame without modifying the original, making it ideal for data preparation and organization tasks.