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 exponential of a Numpy array with the help of some examples.
How to get the exponential in Numpy?
You can use the numpy.exp() function to get the exponential of all elements in a Numpy array. Pass the array as an argument.
The following is the syntax –
numpy.exp(ar)
It returns an array containing the exponential value of each element in the passed array.
Let’s now look at a step-by-step example of using the numpy.exp() 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 ar = np.array([-10, -1, 0, 1, 2, 10]) # display the array print(ar)
Output:
[-10 -1 0 1 2 10]
Here, we used the numpy.array() function to create a Numpy array containing some numbers. You can see that the array contains both positive and negative integer values (along with a zero).
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 exponential using numpy.exp()
To get the exponential of each element in a Numpy array, pass the array as an argument to the numpy.exp() function.
Let’s get the exponential for the array created above.
# get the exponential of each element np.exp(ar)
Output:
array([4.53999298e-05, 3.67879441e-01, 1.00000000e+00, 2.71828183e+00,
7.38905610e+00, 2.20264658e+04])
We get a Numpy array with the exponential value of each element in the array ar.
The numpy.exp() 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
ar = np.array([[1, 0, 1],
[0, 10, 2],
[-10, 0, 1]])
# get the exponential of each element
np.exp(ar)
Output:
array([[2.71828183e+00, 1.00000000e+00, 2.71828183e+00],
[1.00000000e+00, 2.20264658e+04, 7.38905610e+00],
[4.53999298e-05, 1.00000000e+00, 2.71828183e+00]])
You can see that we get the exponential value of each element in the 2D array.
For more on the numpy.exp() 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.
