Pandas DataFrame.mod

The DataFrame.mod method in pandas is used to compute the modulo (remainder) of one DataFrame (or Series) with another DataFrame, Series, or scalar value. It performs element-wise modulo operations.

Syntax

The syntax for DataFrame.mod is:

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

Here, DataFrame refers to the pandas DataFrame on which the modulo operation is performed.

Parameters

Parameter	Description
`other`	Can be a scalar, Series, or DataFrame. The object to be used for the modulo operation.
`axis`	Axis along which the operation is performed. `'columns'` (default) applies the operation across columns, and `'index'` applies it across rows.
`level`	Broadcast across a specified level (useful for MultiIndex). Default is `None`.
`fill_value`	Value to fill missing values in the DataFrame or `other` before performing the operation. Default is `None`.

Returns

A DataFrame containing the result of the element-wise modulo operation.

Examples

Modulo with a Scalar Value

Compute the modulo of all elements in the DataFrame with a scalar value.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'A': [10, 20, 30],
    'B': [15, 25, 35],
    'C': [40, 50, 60]
}
df = pd.DataFrame(data)

# Compute modulo with a scalar value
print("Modulo with scalar value 7:")
result = df.mod(7)
print(result)

Output

Modulo with scalar value 7:
   A  B  C
0  3  1  5
1  6  4  1
2  2  0  4

Modulo with Another DataFrame

Perform element-wise modulo between two DataFrames of the same shape.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'A': [10, 20, 30],
    'B': [15, 25, 35],
    'C': [40, 50, 60]
}
data2 = {
    'A': [3, 4, 5],
    'B': [5, 6, 7],
    'C': [8, 9, 10]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Compute element-wise modulo
print("Element-wise modulo between DataFrames:")
result = df1.mod(df2)
print(result)

Output

Element-wise modulo between DataFrames:
   A  B  C
0  1  0  0
1  0  1  5
2  0  0  0

Modulo with a Series

Compute the modulo between a DataFrame and a Series, aligning the Series with the DataFrame’s columns.

Python Program

import pandas as pd

# Create a DataFrame and a Series
data = {
    'A': [10, 20, 30],
    'B': [15, 25, 35],
    'C': [40, 50, 60]
}
df = pd.DataFrame(data)
series = pd.Series({'A': 4, 'B': 5, 'C': 6})

# Compute modulo with Series
print("Modulo with Series:")
result = df.mod(series)
print(result)

Output

Modulo with Series:
   A  B  C
0  2  0  4
1  0  0  2
2  2  0  0

Handling Missing Values with `fill_value`

Use the fill_value parameter to handle missing values in the DataFrame or other before performing the operation.

Python Program

import pandas as pd
import numpy as np

# Create a DataFrame with missing values
data = {
    'A': [10, 20, np.nan],
    'B': [15, np.nan, 35],
    'C': [40, 50, 60]
}
df = pd.DataFrame(data)

# Compute modulo with a scalar and handle missing values
print("Modulo with scalar value 7, filling missing values with 0:")
result = df.mod(7, fill_value=0)
print(result)

Output

Modulo with scalar value 7, filling missing values with 0:
     A    B  C
0  3.0  1.0  5
1  6.0  0.0  1
2  0.0  0.0  4

Summary

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

Using mod for element-wise modulo operations with scalars, Series, or DataFrames.
Handling missing values using the fill_value parameter.
Customizing the operation with the axis parameter.

The DataFrame.mod method is a powerful tool for performing modulo operations on pandas DataFrames.