NumPy: Compute the natural logarithm of one plus each element of a given array in floating-point accuracy

Last update on August 29 2025 12:44:45 (UTC/GMT +8 hours)

35. Compute log1p (Natural Logarithm of 1 + x)

Write a NumPy program to compute the natural logarithm of one plus each element of a given array in floating-point accuracy.

Sample Solution:

Python Code:

# Importing the NumPy library
import numpy as np

# Creating an array with two very small numbers
x = np.array([1e-99, 1e-100])

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

# Calculating natural logarithm of one plus each element in the array
print("\nNatural logarithm of one plus each element:")
print(np.log1p(x))

Sample Output:

Original array: 
[1.e-099 1.e-100]

Natural logarithm of one plus each element:
[1.e-099 1.e-100]

Explanation:

In the above exercise –

x = np.array([1e-99, 1e-100]): This line creates a NumPy array x with two very small values - 1e-99 and 1e-100.

np.log1p(x): This line calculates the natural logarithm of 1 + x for each element of the x array. Since the values in the x array are very small, the values of 1 + x will be very close to 1, and the natural logarithm of 1 + x will be very close to x. Therefore, the output of np.log1p(x) will be very close to the values of x itself.

For more Practice: Solve these Related Problems:

Implement a function that uses np.log1p to compute the natural logarithm of one plus each element of an array.
Test the function on an array with very small values to ensure numerical stability.
Create a solution that compares np.log1p(x) with np.log(1 + x) for a range of test inputs.
Apply the function on a multi-dimensional array and verify that the output shape is preserved.

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Python-Numpy Code Editor:

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