numpy.log() in Python (original) (raw)

Last Updated : 13 Jan, 2026

numpy.log() is a NumPy function used to compute the natural logarithm (base e) of each element in an input array or a single value. It works element-wise and returns a NumPy array containing the logarithmic results.

This example shows how numpy.log() calculates the natural logarithm of each value in a list of positive numbers.

Python `

import numpy as np x = [1, 2, 4] y = np.log(x) print(y)

`

Output

[0. 0.69314718 1.38629436]

**Explanation: np.log(x) computes the natural logarithm of every element in x.

Syntax

numpy.log(x)

• **Parameters: x - Array like or scalar input containing positive values.
• **Return: Returns an array (or scalar) with the natural logarithm of each element.

Examples

**Example 1: This example calculates the natural logarithm of all elements in a one-dimensional NumPy array.

Python `

import numpy as np a = np.array([1, 3, 9]) r = np.log(a) print(r)

`

Output

[0. 1.09861229 2.19722458]

**Explanation: np.log(a) applies the natural log to each element of a.

**Example 2: This example verifies that log(exp(x)) = x using NumPy’s exponential values.

Python `

import numpy as np v = np.exp([1, 2]) r = np.log(v) print(r)

`

**Explanation: np.log(v) returns the original values because logarithm is the inverse of exponentiation.

**Example 3: This example demonstrates how numpy.log() works on a two-dimensional array.

Python `

import numpy as np m = np.array([[1, 2], [4, 8]]) r = np.log(m) print(r)

`

Output

[[0. 0.69314718] [1.38629436 2.07944154]]

**Explanation: np.log(m) computes the natural logarithm element-wise for the 2D array m.