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DataFrame.apply()This tutorial will show you how we can create new columns in Pandas DataFrame based on the values ​​of other columns in the DataFrame by applying functions to each element of a column or using methods.

import pandas as pd

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708, 103, 343, 565],
        "Name": ["Watch", "Camera", "Phone", "Shoes", "Laptop", "Bed"],
        "Cost": [300, 400, 350, 100, 1000, 400],
        "Discount(%)": [10, 15, 5, 0, 2, 7],
    }
)

print(items_df)

Output:

    Id    Name  Cost  Discount(%)
0  302   Watch   300           10
1  504  Camera   400           15
2  708   Phone   350            5
3  103   Shoes   100            0
4  343  Laptop  1000            2
5  565     Bed   400            7

We will use the DataFrame shown in the code snippet above to demonstrate how to create new columns in a Pandas DataFrame based on the values ​​of other columns in the DataFrame.

Creating New Columns in Pandas DataFrame Based on Other Columns’ Values ​​Element-wise

import pandas as pd

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708, 103, 343, 565],
        "Name": ["Watch", "Camera", "Phone", "Shoes", "Laptop", "Bed"],
        "Actual Price": [300, 400, 350, 100, 1000, 400],
        "Discount(%)": [10, 15, 5, 0, 2, 7],
    }
)

print("Initial DataFrame:")
print(items_df, "\n")

items_df["Final Price"] = items_df["Actual Price"] - (
    (items_df["Discount(%)"] / 100) * items_df["Actual Price"]
)


print("DataFrame after addition of new column")
print(items_df, "\n")

Output:

Initial DataFrame:
    Id    Name  Actual Price  Discount(%)
0  302   Watch           300           10
1  504  Camera           400           15
2  708   Phone           350            5
3  103   Shoes           100            0
4  343  Laptop          1000            2
5  565     Bed           400            7 

DataFrame after addition of new column
    Id    Name  Actual Price  Discount(%)  Final Price
0  302   Watch           300           10        270.0
1  504  Camera           400           15        340.0
2  708   Phone           350            5        332.5
3  103   Shoes           100            0        100.0
4  343  Laptop          1000            2        980.0
5  565     Bed           400            7        372.0 

It calculates the final price of each product by subtracting the discount amount value from Actual Pricethe column of the DataFrame. It then assigns the final price values Series​​to the column items_dfof the DataFrame Final Price.

DataFrame.apply()Create a new column in Pandas DataFrame based on the values ​​of other columns using

import pandas as pd

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708, 103, 343, 565],
        "Name": ["Watch", "Camera", "Phone", "Shoes", "Laptop", "Bed"],
        "Actual_Price": [300, 400, 350, 100, 1000, 400],
        "Discount_Percentage": [10, 15, 5, 0, 2, 7],
    }
)

print("Initial DataFrame:")
print(items_df, "\n")

items_df["Final Price"] = items_df.apply(
    lambda row: row.Actual_Price - ((row.Discount_Percentage / 100) * row.Actual_Price),
    axis=1,
)

print("DataFrame after addition of new column")
print(items_df, "\n")

Output:

Initial DataFrame:
    Id    Name  Actual_Price  Discount_Percentage
0  302   Watch           300                   10
1  504  Camera           400                   15
2  708   Phone           350                    5
3  103   Shoes           100                    0
4  343  Laptop          1000                    2
5  565     Bed           400                    7 

DataFrame after addition of new column
    Id    Name  Actual_Price  Discount_Percentage  Final Price
0  302   Watch           300                   10        270.0
1  504  Camera           400                   15        340.0
2  708   Phone           350                    5        332.5
3  103   Shoes           100                    0        100.0
4  343  Laptop          1000                    2        980.0
5  565     Bed           400                    7        372.0 

It apply()applies the lambda function defined in the method to items_dfeach row of the DataFrame and finally assigns a series of results to the columns items_dfof the DataFrame Final Price.