np.exp() and np.log() in NumPy

Introduction

We use np.exp() to find the exponential of numbers, which means raising the number e (about 2.718) to the power of those numbers. np.log() helps us find the natural logarithm, which is the reverse of the exponential.

When you want to calculate growth like population or money growing exponentially.

When you need to reverse an exponential calculation to find the original number.

When working with probabilities and statistics that use logarithms.

When transforming data to make it easier to analyze or visualize.

Syntax

NumPy

np.exp(x)
np.log(x)

x can be a single number or an array of numbers.

np.exp() calculates e raised to the power of x.

np.log() calculates the natural logarithm (base e) of x. The input x must be positive.

Examples

Calculate e to the power of 1, which equals e (~2.718).

NumPy

import numpy as np

np.exp(1)

Calculate the natural log of e squared, which returns 2.

NumPy

import numpy as np

np.log(np.exp(2))

Calculate the exponential for each element in the array.

NumPy

import numpy as np

arr = np.array([0, 1, 2])
np.exp(arr)

Calculate the natural log for each element, returning [0, 1, 2].

NumPy

import numpy as np

np.log(np.array([1, np.e, np.e**2]))

Sample Program

This program shows how np.exp() raises e to each number in the array, and then np.log() reverses it back to the original numbers.

NumPy

import numpy as np

# Create an array of values
values = np.array([0, 1, 2, 3])

# Calculate exponential of each value
exp_values = np.exp(values)

# Calculate natural log of the exponential values
log_values = np.log(exp_values)

print("Original values:", values)
print("Exponential values:", exp_values)
print("Log of exponential values:", log_values)

Important Notes

Input to np.log() must be positive; otherwise, it will return an error or NaN.

These functions work element-wise on arrays, so you can use them on lists of numbers easily.

Summary

np.exp() calculates e raised to the power of a number or array.

np.log() finds the natural logarithm, which is the inverse of the exponential.

They are useful for growth calculations, reversing exponentials, and data transformations.