NumPy: Compute natural, base 10, and base 2 logarithms for all elements in a given array

Last update on August 29 2025 12:44:46 (UTC/GMT +8 hours)

34. Compute Logarithms (Natural, Base 10, Base 2)

Write a NumPy program to compute natural, base 10, and base 2 logarithms for all elements in a given array.

Sample Solution:

Python Code:

# Importing the NumPy library
import numpy as np

# Creating an array consisting of 1, e, and e^2
x = np.array([1, np.e, np.e**2])

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

# Calculating natural logarithm (base e) of the array elements
print("\nNatural log =", np.log(x))

# Calculating common logarithm (base 10) of the array elements
print("Common log =", np.log10(x))

# Calculating base 2 logarithm of the array elements
print("Base 2 log =", np.log2(x))

Sample Output:

Original array: 
[1.         2.71828183 7.3890561 ]

Natural log = [0. 1. 2.]
Common log = [0.         0.43429448 0.86858896]
Base 2 log = [0.         1.44269504 2.88539008]

Explanation:

in the above code –

x = np.array([1, np.e, np.e**2]): This line creates a NumPy array x with 3 elements, where the first element is 1, the second element is the mathematical constant e (approximately equal to 2.71828), and the third element is e raised to the power of 2.

np.log(x): np.log(x) computes the natural logarithm (base e) of each element in the array x.

np.log10(x): np.log10(x) computes the common logarithm (base 10) of each element in the array x.

np.log2(x): np.log2(x) computes the base 2 logarithm of each element in the array x.

For more Practice: Solve these Related Problems:

Implement a function that computes np.log, np.log10, and np.log2 for a given array of positive numbers.
Test the function on a multi-dimensional array and verify that the logarithms are computed element-wise.
Create a solution that returns a dictionary with keys for natural, common, and base 2 logs.
Compare the computed logarithms with manually calculated values for a set of test numbers.

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