Write a program in Python to print the power of all the elements in a given series

Computing the power of each element in a Pandas Series means raising each element to itself (x^x). This tutorial demonstrates three different approaches to achieve this operation.

Input − Assume, you have a series,

0    1
1    2
2    3
3    4

Output − And, the result for the power of all elements in a series is,

0      1
1      4
2     27
3    256

Using transform() with Lambda Function

The transform() method applies a function to each element of the series. We use a lambda function to calculate x^x for each element ?

import pandas as pd

data = pd.Series([1, 2, 3, 4])
print("Original Series:")
print(data)

result = data.transform(lambda x: x**x)
print("\nPower of elements:")
print(result)

Original Series:
0    1
1    2
2    3
3    4
dtype: int64

Power of elements:
0      1
1      4
2     27
3    256
dtype: int64

Using Loop with math.pow()

This approach iterates through each element using items() and applies math.pow() to calculate the power ?

import pandas as pd
import math as m

data = pd.Series([1, 2, 3, 4])
power_list = []

for index, value in data.items():
    power_list.append(m.pow(value, value))

result = pd.Series(power_list)
print("Power of elements:")
print(result)

Power of elements:
0      1.0
1      4.0
2     27.0
3    256.0
dtype: float64

Using apply() Method

The apply() method provides another clean way to perform element-wise operations ?

import pandas as pd

data = pd.Series([1, 2, 3, 4])
result = data.apply(lambda x: x**x)
print("Power of elements:")
print(result)

Power of elements:
0      1
1      4
2     27
3    256
dtype: int64

Comparison

Conclusion

Use transform() or apply() with lambda functions for efficient power calculations in Pandas Series. The loop approach offers more control but is less efficient for large datasets.