Python - Add a new column with constant value to Pandas DataFrame

To add a new column with a constant value to a Pandas DataFrame, use the square bracket notation (index operator) and assign the desired value. This operation broadcasts the constant value across all rows in the DataFrame.

Syntax

dataframe['new_column_name'] = constant_value

Creating a Sample DataFrame

First, let's create a DataFrame with sample car data −

import pandas as pd

# Creating a DataFrame with car information
dataFrame = pd.DataFrame({
    "Car": ['Bentley', 'Lexus', 'BMW', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 110, 150, 80, 200, 90]
})

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

Original DataFrame:
       Car  Cubic_Capacity  Reg_Price  Units_Sold
0  Bentley            2000       7000         100
1    Lexus            1800       1500         110
2      BMW            1500       5000         150
3  Mustang            2500       8000          80
4 Mercedes            2200       9000         200
5   Jaguar            3000       6000          90

Adding a Column with Constant Value

Now we'll add a new column called 'Mileage' with a constant value of 15 for all rows −

import pandas as pd

# Creating DataFrame
dataFrame = pd.DataFrame({
    "Car": ['Bentley', 'Lexus', 'BMW', 'Mustang', 'Mercedes', 'Jaguar'],
    "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],
    "Units_Sold": [100, 110, 150, 80, 200, 90]
})

# Adding new column with constant value
dataFrame['Mileage'] = 15

print("Updated DataFrame with new column:")
print(dataFrame)

Updated DataFrame with new column:
       Car  Cubic_Capacity  Reg_Price  Units_Sold  Mileage
0  Bentley            2000       7000         100       15
1    Lexus            1800       1500         110       15
2      BMW            1500       5000         150       15
3  Mustang            2500       8000          80       15
4 Mercedes            2200       9000         200       15
5   Jaguar            3000       6000          90       15

Multiple Constant Columns

You can also add multiple columns with different constant values −

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['Bentley', 'Lexus', 'BMW'],
    "Price": [7000, 1500, 5000]
})

# Adding multiple constant columns
dataFrame['Category'] = 'Luxury'
dataFrame['Available'] = True
dataFrame['Rating'] = 4.5

print(dataFrame)

     Car  Price Category  Available  Rating
0  Bentley   7000   Luxury       True     4.5
1    Lexus   1500   Luxury       True     4.5
2      BMW   5000   Luxury       True     4.5

Conclusion

Adding a constant value column to a Pandas DataFrame is straightforward using bracket notation. The constant value is automatically broadcasted to all rows, making it an efficient way to add default values or categorical labels to your dataset.