Pandas DataFrame.mul

Pandas DataFrame.mul

The DataFrame.mul method in pandas is used to perform element-wise multiplication of a DataFrame with another DataFrame, Series, or scalar value. This method provides flexible options to handle mismatched data and axes.

Syntax

The syntax for DataFrame.mul is:

DataFrame.mul(other, axis='columns', level=None, fill_value=None)

Here, DataFrame refers to the pandas DataFrame being multiplied.

Parameters

ParameterDescription
otherThe object to multiply with, which can be a DataFrame, Series, or scalar value.
axisDetermines the alignment axis. Can be 'index' (0) or 'columns' (1). Defaults to 'columns'.
levelUsed to broadcast along a particular level, matching index values on a MultiIndex.
fill_valueValue to fill in for missing data before performing multiplication. By default, no filling is performed.

Returns

A DataFrame resulting from the element-wise multiplication.

Examples

Multiplying by a Scalar

Multiply all elements in a DataFrame by a scalar value.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Product A': [10, 20, 30],
    'Product B': [15, 25, 35]
}
df = pd.DataFrame(data)

# Multiply by a scalar
print("DataFrame after multiplying by 2:")
df_scaled = df.mul(2)
print(df_scaled)

Output

DataFrame after multiplying by 2:
   Product A  Product B
0         20         30
1         40         50
2         60         70

Element-Wise Multiplication with Another DataFrame

Perform element-wise multiplication with another DataFrame.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'Product A': [10, 20, 30],
    'Product B': [15, 25, 35]
}
data2 = {
    'Product A': [1, 2, 3],
    'Product B': [0.5, 1, 1.5]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Element-wise multiplication
print("Element-wise multiplication:")
df_multiplied = df1.mul(df2)
print(df_multiplied)

Output

Element-wise multiplication:
   Product A  Product B
0       10.0        7.5
1       40.0       25.0
2       90.0       52.5

Multiplication with a Series

Multiply a DataFrame with a Series along a specified axis.

Python Program

import pandas as pd

# Create a DataFrame and a Series
data = {
    'Product A': [10, 20, 30],
    'Product B': [15, 25, 35]
}
multipliers = pd.Series([1, 2, 3], index=[0, 1, 2])
df = pd.DataFrame(data)

# Multiply with Series along the index
print("Multiplication with a Series:")
df_multiplied = df.mul(multipliers, axis='index')
print(df_multiplied)

Output

Multiplication with a Series:
   Product A  Product B
0         10       15.0
1         40       50.0
2         90      105.0

Using fill_value for Missing Data

Use the fill_value parameter to handle missing data before multiplication.

Python Program

import pandas as pd

# Create two DataFrames with missing values
data1 = {
    'Product A': [10, None, 30],
    'Product B': [15, 25, None]
}
data2 = {
    'Product A': [1, 2, 3],
    'Product B': [0.5, None, 1.5]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Multiply with fill_value
print("Multiplication with fill_value=1:")
df_multiplied = df1.mul(df2, fill_value=1)
print(df_multiplied)

Output

Multiplication with fill_value=1:
   Product A  Product B
0       10.0        7.5
1        2.0       25.0
2       90.0        1.5

Summary

In this tutorial, we explored the DataFrame.mul method in pandas. Key takeaways include:

  • Performing element-wise multiplication with DataFrames, Series, or scalar values.
  • Using the axis parameter to control alignment for multiplication with Series.
  • Handling missing data with the fill_value parameter.

The DataFrame.mul method is a flexible tool for performing element-wise multiplication in pandas, making it ideal for data analysis tasks.

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