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Get the Natural Log of Each Element in Numpy Array

The Numpy library in Python comes with a number of built-in functions to perform common mathematical operations on arrays. In this tutorial, we will look at one such function that helps us get the element-wise natural log of a Numpy array with the help of some examples.

How to get the natural log in Numpy?

natural log of each element in numpy array

You can use the numpy.log() function to get the natural logarithm of each element in a Numpy array. Pass the array as an argument.

The following is the syntax –

numpy.log(ar)

It returns an array containing the natural log value of each element in the passed array.

Let’s now look at a step-by-step example of using the numpy.log() function.

Step 1 – Create a Numpy array

First, we will create a Numpy array that we will use throughout this tutorial.

import numpy as np

# create numpy array
e = np.exp(1)
ar = np.array([1, 2, e, e**2, 10])
# display the array
print(ar)

Output:

[ 1.          2.          2.71828183  7.3890561  10.        ]

Here, we used the numpy.array() function to create a Numpy array containing some numbers. You can see that the array contains some integers and some lower powers of e (we use numpy.exp() to get the value of e).

Step 2 – Get the natural log using numpy.log()

To get the natural log of each element in a Numpy array, pass the array as an argument to the numpy.log() function.

Let’s get the natural log for the array created above.

# get the log of each element
np.log(ar)

Output:

array([0.        , 0.69314718, 1.        , 2.        , 2.30258509])

We get a Numpy array with the natural log value of each element in the array ar.

The numpy.log() function works similarly on higher-dimensional arrays. For example, let’s apply this function to a 2D array of some numbers.

# create 2D numpy array
e = np.exp(1)
ar = np.array([[1, e, 1],
               [e**2, 10, e**2],
               [4, e**4, 5]])
# get the log of each element
np.log(ar)

Output:

array([[0.        , 1.        , 0.        ],
       [2.        , 2.30258509, 2.        ],
       [1.38629436, 4.        , 1.60943791]])

You can see that we get the natural log value of each element in the 2D array.

For more on the numpy.log() function, refer to its documentation.

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Author

  • Piyush is a data scientist passionate about using data to understand things better and make informed decisions. In the past, he's worked as a Data Scientist for ZS and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.