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 log10 (logarithm with base 10) of a Numpy array with the help of some examples.

## How to get the log10 of values in a Numpy array?

You can use the `numpy.log10()`

function to get the log10 (logarithm with base 10) of each element in a Numpy array. Pass the array as an argument.

The following is the syntax –

numpy.log10(ar)

It returns an array containing the base 2 logarithm of each element in the passed array.

Let’s now look at a step-by-step example of using the `numpy.log10()`

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, 10, 100, 1000]) # display the array print(ar)

Output:

[ 1 2 10 100 1000]

Here, we used the `numpy.array()`

function to create a Numpy array containing some numbers. You can see that the array contains some integers (note that there are some values that are powers of ten – 10, 100, and 1000).

**Data Science Programs By Skill Level**

**Introductory** ⭐

- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science

**Intermediate ⭐⭐⭐**

- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization

**Advanced ⭐⭐⭐⭐⭐**

- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science

**🔎 Find Data Science Programs 👨💻 111,889 already enrolled**

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

### Step 2 – Get the log10 using `numpy.log10()`

To get the base 10 log of each element in a Numpy array, pass the array as an argument to the `numpy.log10()`

function.

Let’s get the base 10 log for the array created above.

# get the log10 of each element np.log10(ar)

Output:

array([0. , 0.30103, 1. , 2. , 3. ])

We get a Numpy array with the base 10 logarithm value of each element in the array `ar`

.

The `numpy.log10()`

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, 10, 1], [100, 10, 100], [2, 50, 10]]) # get the log10 of each element np.log10(ar)

Output:

array([[0. , 1. , 0. ], [2. , 1. , 2. ], [0.30103, 1.69897, 1. ]])

You can see that we get the base 10 log value of each element in the 2D array.

For more on the `numpy.log10()`

function, refer to its documentation.

You might also be interested in –

- Numpy – Get the Sign of Each Element in Array
- Get the Median of Numpy Array – (With Examples)
- Numpy – Get Standard Deviation of Array Values
- Numpy – Get Min Value in Array

**Subscribe to our newsletter for more informative guides and tutorials. ****We do not spam and you can opt out any time.**