Pandas DataFrame.rpow

The DataFrame.rpow method in pandas performs the reverse power operation. It calculates other ** DataFrame, where other is a scalar, sequence, or another DataFrame. This method is useful for element-wise reverse exponentiation.

Syntax

The syntax for DataFrame.rpow is:

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

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

Parameters

Parameter	Description
`other`	A scalar, sequence, Series, or DataFrame to compute `other ** DataFrame`.
`axis`	The axis along which to align the `other` object. Can be `'index'` (0) or `'columns'` (1). Default is `'columns'`.
`level`	If the axis is a MultiIndex (hierarchical), this parameter specifies the level to align with.
`fill_value`	Specifies the value to fill in for missing elements in either DataFrame. Default is `None`.

Returns

A DataFrame resulting from the element-wise reverse power operation.

Examples

Basic Reverse Power Operation with a Scalar

Use rpow with a scalar to compute scalar ** DataFrame.

Python Program

import pandas as pd

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

# Perform reverse power operation with a scalar
print("Reverse Power Operation with Scalar (2):")
result = df.rpow(2)
print(result)

Output

Reverse Power Operation with Scalar (2):
          A         B
0  2.000000  0.062500
1  1.414214  0.031250
2  1.259921  0.015625

Reverse Power Operation with a Series

Use rpow with a Series to compute element-wise reverse power.

Python Program

import pandas as pd

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

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

# Perform reverse power operation
print("Reverse Power Operation with Series:")
result = df.rpow(series, axis='index')
print(result)

Output

Reverse Power Operation with Series:
           A              B
0  10.000000  10000.000000
1   4.472136    400.000000
2   3.107232     36.000000

Handling Missing Values with `fill_value`

Specify a fill_value to handle missing elements during the operation.

Python Program

import pandas as pd
import numpy as np

# Create a DataFrame with NaN values
data = {
    'A': [1, 2, np.nan],
    'B': [4, 5, 6]
}
df = pd.DataFrame(data)

# Perform reverse power operation with a scalar and fill_value
print("Reverse Power Operation with fill_value=1:")
result = df.rpow(2, fill_value=1)
print(result)

Output

Reverse Power Operation with fill_value=1:
          A         B
0  2.000000  0.062500
1  1.414214  0.031250
2  1.000000  0.015625

Using `axis` Parameter

Align other with rows or columns using the axis parameter.

Python Program

import pandas as pd

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

# Perform reverse power operation along columns
print("Reverse Power Operation with axis='columns':")
result = df.rpow([10, 20], axis='columns')
print(result)

Output

Reverse Power Operation with axis='columns':
           A          B
0  10.000000  10000.000000
1   4.472136    400.000000
2   3.162278     36.000000

Summary

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

Using rpow to compute reverse power operations (other ** DataFrame).
Handling missing values with fill_value.
Aligning operations using the axis parameter.

The DataFrame.rpow method is a flexible tool for performing reverse exponentiation in pandas DataFrames.