Pandas DataFrame Multiplication | Python Programming

Introduction

This lab will demonstrate how to use the mul() method in the Pandas DataFrame class. The mul() method is used to multiply a DataFrame with another DataFrame, Series, or scalar value element-wise. It returns a new DataFrame with the result of the multiplication operation.

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Skills Graph

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Import the required libraries

import pandas as pd

First, we need to import the Pandas library.

Create the initial DataFrame

df1 = pd.DataFrame({'a': [1, 5, 6],'b': [4, 6, 5],'c': [2, 8, 7]})

Create a DataFrame called df1 with three columns a, b, and c, containing the given values.

Multiply the DataFrame by a scalar value

df2 = df1.mul(2)
print(df2)

Multiply the DataFrame df1 by a scalar value of 2 using the mul() method. Print the result.

Multiply the DataFrame by another DataFrame

df3 = pd.DataFrame({'a': [2, 1, 1],'b': [1, 5, 8],'c': [7, 5, 6]})
df4 = df1.mul(df3)
print(df4)

Create another DataFrame called df3 with the given values. Multiply df1 by df3 using the mul() method. Print the result.

Handling missing values

df5 = pd.DataFrame({'a': [None, 1, 1],'b': [None, 5, 8]})
df6 = df1.mul(df5, fill_value=1)
print(df6)

Create another DataFrame df5 with some missing values. Multiply df1 by df5 using the mul() method. Use the fill_value parameter to replace missing values with 1. Print the result.

Summary

In this lab, we learned how to use the mul() method in the Pandas DataFrame class to multiply DataFrames element-wise. We saw examples of multiplying a DataFrame by a scalar value, another DataFrame, and handling missing values. The mul() method is a useful tool for performing element-wise multiplication operations on DataFrames.

Pandas DataFrame Mul Method