NumPy: Compute e^x, element-wise of a given array

Last update on August 29 2025 12:48:23 (UTC/GMT +8 hours)

31. Element-wise Exponential Function

Write a NumPy program to compute e^x, element-wise of a given array.

Sample Solution:

Python Code:

# Importing the NumPy library
import numpy as np

# Creating an array of float32 type
x = np.array([1., 2., 3., 4.], np.float32)

# Displaying the original array
print("Original array: ")
print(x)

# Calculating exponential (e^x) for each element of the array x
print("\ne^x, element-wise of the said:")
r = np.exp(x)
print(r)

Sample Output:

Original array: 
[1. 2. 3. 4.]

e^x, element-wise of the said:
[ 2.7182817  7.389056  20.085537  54.59815  ]

Explanation:

In the above code –

x = np.array([1., 2., 3., 4.], np.float32): This code creates a one-dimensional NumPy array x of data type float32 is created with the values [1., 2., 3., 4.].

r = np.exp(x) – Here np.exp() function is applied to x, which returns a new array r containing the exponential value of each element in x. The exponential function raises the constant e to the power of each element in x, so the resulting array r will have the values [2.718282 , 7.389056 , 20.085537, 54.598152].

For more Practice: Solve these Related Problems:

Implement a function that computes the exponential of each element in an array using np.exp.
Test the function on arrays with both positive and negative values to verify the exponential behavior.
Create a solution that compares the output of np.exp with a Taylor series approximation for small inputs.
Apply the exponential function to a multi-dimensional array to ensure element-wise operation.

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