Pandas DataFrame.div

The DataFrame.div method in pandas is used to perform element-wise division of a DataFrame by another DataFrame, Series, or scalar. This method is flexible and allows division along rows or columns.

Syntax

The syntax for DataFrame.div is:

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

Here, DataFrame refers to the pandas DataFrame that is being divided.

Parameters

Parameter	Description
`other`	The object to divide by. Can be a scalar, Series, or DataFrame.
`axis`	The axis to align the `other` object with: `'columns'` (default) or `'index'`.
`level`	If the axis is a MultiIndex, this specifies the level to broadcast over.
`fill_value`	Value to fill in missing entries before division. By default, missing values result in `NaN`.

Returns

A DataFrame with element-wise division results.

Examples

Dividing by a Scalar

Divide all elements of a DataFrame by a scalar value.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'A': [10, 20, 30],
    'B': [40, 50, 60],
    'C': [70, 80, 90]
}
df = pd.DataFrame(data)

# Divide the DataFrame by a scalar
print("Dividing DataFrame by 10:")
df_divided = df.div(10)
print(df_divided)

Output

Dividing DataFrame by 10:
     A    B    C
0  1.0  4.0  7.0
1  2.0  5.0  8.0
2  3.0  6.0  9.0

Dividing by Another DataFrame

Divide one DataFrame by another DataFrame element-wise.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'A': [10, 20, 30],
    'B': [40, 50, 60],
    'C': [70, 80, 90]
}
data2 = {
    'A': [2, 4, 6],
    'B': [8, 10, 12],
    'C': [14, 16, 18]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Element-wise division
print("Dividing one DataFrame by another:")
df_divided = df1.div(df2)
print(df_divided)

Output

Dividing one DataFrame by another:
     A    B    C
0  5.0  5.0  5.0
1  5.0  5.0  5.0
2  5.0  5.0  5.0

Dividing with Alignment Along Rows

Divide a DataFrame by a Series along rows (axis=0).

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'A': [10, 20, 30],
    'B': [40, 50, 60],
    'C': [70, 80, 90]
}
df = pd.DataFrame(data)

# Create a Series to divide
s = pd.Series([10, 20, 30], index=[0, 1, 2])

# Perform division along rows
print("Dividing DataFrame by a Series along rows:")
df_divided = df.div(s, axis=0)
print(df_divided)

Output

Dividing DataFrame by a Series along rows:
     A    B    C
0  1.0  4.0  7.0
1  1.0  2.5  4.0
2  1.0  2.0  3.0

Handling Missing Values with `fill_value`

Use the fill_value parameter to fill missing values before division.

Python Program

import pandas as pd

# Create two DataFrames with missing values
data1 = {
    'A': [10, None, 30],
    'B': [40, 50, None],
    'C': [70, 80, 90]
}
data2 = {
    'A': [2, 4, 6],
    'B': [8, 10, 12],
    'C': [14, 16, 18]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Element-wise division with fill_value
print("Dividing with fill_value=1:")
df_divided = df1.div(df2, fill_value=1)
print(df_divided)

Output

Dividing with fill_value=1:
     A    B    C
0  5.0  5.0  5.0
1  0.0  5.0  5.0
2  5.0  0.0  5.0

Summary

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

Performing scalar division on all DataFrame elements.
Dividing DataFrames element-wise.
Using alignment along rows or columns.
Handling missing values with the fill_value parameter.

The DataFrame.div method is a powerful tool for applying element-wise division in pandas DataFrames.