How to Multiply two or more Columns in Pandas [5 Ways]

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# How to Multiply two or more Columns in Pandas

To multiply two or more columns in a Pandas DataFrame:

Select the columns using bracket notation.
Use the multiplication * operator to multiply the columns.
Optionally assign the multiplication result to a new DataFrame column.

main.py

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import pandas as pd

df = pd.DataFrame({
    'price': [10, 20, 30, 40],
    'amount': [2, 4, 5, 6],
    'product': ['apple', 'pear', 'apple', 'banana']
})

print(df)

df['total'] = df['price'] * df['amount']

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following result.

shell

Copied!
   price  amount product
0     10       2   apple
1     20       4    pear
2     30       5   apple
3     40       6  banana
--------------------------------------------------
   price  amount product  total
0     10       2   apple     20
1     20       4    pear     80
2     30       5   apple    150
3     40       6  banana    240

multiply two columns in pandas

The code sample shows how to multiply the price and amount columns of the DataFrame and assign the results to the total column.

The same approach can be used to multiply more than two columns in a DataFrame.

To get the correct multiplication result, make sure the column values are of type int or float.

main.py

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import pandas as pd

df = pd.DataFrame({
    'price': ['10', '20', '30', '40'],
    'amount': [2, 4, 5, 6],
    'product': ['apple', 'pear', 'apple', 'banana']
})

print(df)

df['total'] = df['price'].astype('int') * df['amount'].astype('int')

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
  price  amount product
0    10       2   apple
1    20       4    pear
2    30       5   apple
3    40       6  banana
--------------------------------------------------
  price  amount product  total
0    10       2   apple     20
1    20       4    pear     80
2    30       5   apple    150
3    40       6  banana    240

convert to int before multiplying

The values in the price column are strings, so we used the astype() method to convert them to integers before multiplying.

# Multiply two Columns in Pandas based on a condition

You can use the DataFrame.where() method if you need to multiply two columns in a DataFrame based on a condition.

main.py

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import pandas as pd

df = pd.DataFrame({
    'price': [10, 20, 30, 40],
    'amount': [2, 4, 5, 6],
    'product': ['apple', 'pear', 'apple', 'banana']
})

print(df)

total = df['price'] * df['amount']

df['total'] = total.where(df['product'] == 'apple', other=0)

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
   price  amount product
0     10       2   apple
1     20       4    pear
2     30       5   apple
3     40       6  banana
--------------------------------------------------
   price  amount product  total
0     10       2   apple     20
1     20       4    pear      0
2     30       5   apple    150
3     40       6  banana      0

multiply two columns based on condition

If the product column stores a value of apple then the multiplication result is added to the total column, otherwise, 0 is returned.

main.py

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df['total'] = total.where(df['product'] == 'apple', other=0)

In other words, entries for which the supplied condition is False are replaced with the values from the other argument.

# Multiply two Columns in Pandas based on a condition using `apply()`

You can also use the DataFrame.apply() method to multiply two columns in a DataFrame based on a condition.

main.py

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import pandas as pd

df = pd.DataFrame({
    'price': [10, 20, 30, 40],
    'amount': [2, 4, 5, 6],
    'product': ['apple', 'pear', 'apple', 'banana']
})

print(df)

print('-' * 50)

df['total'] = df.apply(
    lambda row: (
        row['price'] * row['amount']
        if row['product'] == 'apple'
        else 0
    ),
    axis=1
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
   price  amount product
0     10       2   apple
1     20       4    pear
2     30       5   apple
3     40       6  banana
--------------------------------------------------
   price  amount product  total
0     10       2   apple     20
1     20       4    pear      0
2     30       5   apple    150
3     40       6  banana      0

multiply two columns based on condition using apply

The DataFrame.apply() method applies a function along an axis of the DataFrame.

Setting the axis argument to 1 is very important because we want to pass each row to the lambda function.

main.py

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df['total'] = df.apply(
    lambda row: (
        row['price'] * row['amount']
        if row['product'] == 'apple'
        else 0
    ),
    axis=1
)

The axis argument determines the axis along which the function is applied.

By default, the axis argument is set to 0, which means that it is applied to each column.

We set the axis argument to 1 to apply the function to each row.

The function multiplies the values in the price and amount columns if the corresponding product value is equal to "apple", otherwise, 0 is returned.

You don't necessarily have to use an inline lambda function.

main.py

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import pandas as pd

df = pd.DataFrame({
    'price': [10, 20, 30, 40],
    'amount': [2, 4, 5, 6],
    'product': ['apple', 'pear', 'apple', 'banana']
})

print(df)

print('-' * 50)


def get_total(row):
    if row['product'] == 'apple':
        return row['price'] * row['amount']
    else:
        return 0


df['total'] = df.apply(get_total, axis=1)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
   price  amount product
0     10       2   apple
1     20       4    pear
2     30       5   apple
3     40       6  banana
--------------------------------------------------
   price  amount product  total
0     10       2   apple     20
1     20       4    pear      0
2     30       5   apple    150
3     40       6  banana      0

The get_total function gets called with each row.

The function returns the multiplication result if the corresponding product is equal to "apple", otherwise, 0 is returned.

# Multiply two columns in a `DataFrame` using `mul()`

You can also use the mul() method to multiply two columns in a DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'price': [10, 20, 30, 40],
    'amount': [2, 4, 5, 6],
    'product': ['apple', 'pear', 'apple', 'banana']
})

print(df)

print('-' * 50)

df['total'] = df['price'].mul(df['amount'])

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
   price  amount product
0     10       2   apple
1     20       4    pear
2     30       5   apple
3     40       6  banana
--------------------------------------------------
   price  amount product  total
0     10       2   apple     20
1     20       4    pear     80
2     30       5   apple    150
3     40       6  banana    240

The mul() method is equivalent to df['A'] * df['B'], however, it has a fill_value argument for missing data in either of the inputs.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame({
    'price': [10, 20, 30, 40],
    'amount': [np.nan, 4, 5, np.nan],
    'product': ['apple', 'pear', 'apple', 'banana']
})

print(df)

print('-' * 50)

df['total'] = df['price'].mul(df['amount'], fill_value=0)

print(df)

Running the code sample produces the following output.

shell

Copied!
   price  amount product
0     10     NaN   apple
1     20     4.0    pear
2     30     5.0   apple
3     40     NaN  banana
--------------------------------------------------
   price  amount product  total
0     10     NaN   apple    0.0
1     20     4.0    pear   80.0
2     30     5.0   apple  150.0
3     40     NaN  banana    0.0

The fill_value argument fills missing (NaN) values.

main.py

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df['total'] = df['price'].mul(df['amount'], fill_value=0)

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How to Multiply two or more Columns in Pandas [5 Ways]

# Table of Contents