Pandas DataFrame.pow

The DataFrame.pow method in pandas is used to perform element-wise exponentiation (power) of DataFrame values with another DataFrame, Series, or scalar value. This method is flexible and allows alignment on specified axes and levels.

Syntax

The syntax for DataFrame.pow is:

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

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

Parameters

Parameter	Description
`other`	A scalar, DataFrame, or Series to raise the values of the DataFrame to.
`axis`	Defines the axis along which to align the `other`. Default is `'columns'`.
`level`	Broadcast across a level, matching `other` if the axis is a MultiIndex.
`fill_value`	Value to use to fill missing entries in the DataFrame or `other`. Default is `None`.

Returns

A DataFrame with the result of the power operation.

Examples

Raising a DataFrame to a Scalar Power

Raise all elements of a DataFrame to the power of a scalar value.

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)

# Raise each element to the power of 2
print("DataFrame raised to the power of 2:")
df_pow = df.pow(2)
print(df_pow)

Output

DataFrame raised to the power of 2:
    A   B    C
0   1  16   49
1   4  25   64
2   9  36   81

Element-Wise Power Using Another DataFrame

Perform element-wise exponentiation with another DataFrame.

Python Program

import pandas as pd

# Create two DataFrames
data1 = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}
data2 = {
    'A': [2, 2, 2],
    'B': [3, 3, 3],
    'C': [1, 1, 1]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform element-wise power operation
print("Element-wise power operation:")
df_result = df1.pow(df2)
print(df_result)

Output

Element-wise power operation:
      A      B  C
0     1     64  7
1     4    125  8
2     9    216  9

Using the `fill_value` Parameter

Use fill_value to handle missing values during the power operation.

Python Program

import pandas as pd

# Create two DataFrames with missing values
data1 = {
    'A': [1, None, 3],
    'B': [4, 5, None],
    'C': [7, 8, 9]
}
data2 = {
    'A': [2, 2, 2],
    'B': [3, 3, 3],
    'C': [1, 1, 1]
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

# Perform power operation with fill_value
print("Power operation with fill_value:")
df_result = df1.pow(df2, fill_value=1)
print(df_result)

Output

Power operation with fill_value:
      A      B    C
0     1     64  7.0
1     1    125  8.0
2     9      1  9.0

Applying Power Along a Specific Axis

Use the axis parameter to specify the alignment of the operation.

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([2, 3, 1], index=['A', 'B', 'C'])

# Perform power operation along columns
print("Power operation along columns:")
df_result = df.pow(series, axis='columns')
print(df_result)

Output

Power operation along columns:
      A      B      C
0     1     64    7.0
1     4    125    8.0
2     9    216    9.0

Summary

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

Performing scalar and element-wise exponentiation using pow.
Handling missing values with the fill_value parameter.
Aligning operations along specific axes.

The DataFrame.pow method is a powerful and flexible way to perform element-wise exponentiation in pandas.