Pandas DataFrame.rsub

The DataFrame.rsub method in pandas performs the reverse subtraction of a DataFrame and another object (scalar, Series, or DataFrame). This means it subtracts the values in the DataFrame from the other object.

Syntax

The syntax for DataFrame.rsub is:

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

Here, DataFrame refers to the pandas DataFrame involved in the operation.

Parameters

Parameter	Description
`other`	The object to be subtracted from (scalar, Series, or DataFrame).
`axis`	Determines the axis for the operation. Defaults to `'columns'` (1). Can be `0` or `'index'` for row-wise operation.
`level`	If the axis is a MultiIndex (hierarchical), this specifies the level to broadcast along. Defaults to `None`.
`fill_value`	Value to substitute for missing data. Defaults to `None`.

Returns

A DataFrame where each element is the result of the reverse subtraction.

Examples

Reverse Subtraction with a Scalar

Subtract each value in the DataFrame from a scalar.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}
df = pd.DataFrame(data)

# Perform reverse subtraction with a scalar
result = df.rsub(10)
print("Result of reverse subtraction with scalar 10:")
print(result)

Output

Result of reverse subtraction with scalar 10:
     A    B    C
0   9    6    3
1   8    5    2
2   7    4    1

Reverse Subtraction with Another DataFrame

Perform reverse subtraction between two DataFrames.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}
data2 = {
    'A': [10, 20, 30],
    'B': [40, 50, 60],
    'C': [70, 80, 90]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform reverse subtraction
result = df1.rsub(df2)
print("Result of reverse subtraction between DataFrames:")
print(result)

Output

Result of reverse subtraction between DataFrames:
     A     B     C
0    9    36    63
1   18    45    72
2   27    54    81

Reverse Subtraction with a Series

Perform reverse subtraction between a DataFrame and a Series.

Python Program

import pandas as pd

# Create a DataFrame and a Series
data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}
df = pd.DataFrame(data)
series = pd.Series([10, 20, 30], index=['A', 'B', 'C'])

# Perform reverse subtraction
result = df.rsub(series, axis='columns')
print("Result of reverse subtraction with a Series:")
print(result)

Output

Result of reverse subtraction with a Series:
     A     B     C
0    9    16    23
1    8    15    22
2    7    14    21

Using `fill_value` to Handle Missing Data

Use the fill_value parameter to handle missing data during reverse subtraction.

Python Program

import pandas as pd

# Create two DataFrames with missing data
data1 = {
    'A': [1, 2, None],
    'B': [4, 5, 6]
}
data2 = {
    'A': [10, 20, 30],
    'B': [None, 50, 60]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform reverse subtraction with fill_value
result = df1.rsub(df2, fill_value=0)
print("Result of reverse subtraction with fill_value=0:")
print(result)

Output

Result of reverse subtraction with fill_value=0:
      A     B
0   9.0  -4.0
1  18.0  45.0
2  30.0  54.0

Summary

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

Performing reverse subtraction with scalars, Series, or other DataFrames.
Using the fill_value parameter to handle missing data.
Specifying the axis parameter for column-wise or row-wise operations.

The DataFrame.rsub method is a useful tool for performing reverse subtraction operations in pandas.