The Numpy library in Python comes with a number of built-in functions to manipulate the data in arrays. In this tutorial, we will look at a function that helps us set all the values in a Numpy array to zero.
How to set all values to zero in Numpy?
![Set all values to zero in numpy array](https://datascienceparichay.com/wp-content/uploads/2022/07/Numpy-set-all-values-to-zero-in-array.png)
You can use the numpy.ndarray.fill()
function to set all the values in a Numpy array to zero. Pass 0
as the argument (this is the value used to fill all the values in the array).
The following is the syntax –
# set all values in numpy array ar to 0 ar.fill(0)
It modifies the array in-place, filling each value with zero (the passed value).
Let’s now look at a step-by-step example of using the above 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 a numpy array ar = np.array([-2, -1, 0, 1, 2, 3, -4]) # display the array print(ar)
Output:
[-2 -1 0 1 2 3 -4]
Here, we used the numpy.array()
function to create a Numpy array containing some numbers.
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 – Set each value to 0
using numpy.ndarray.fill()
Apply the numpy.ndarray.fill()
function on the array and pass 0
as the parameter to set each value to zero in the array.
Let’s apply this function to the array created above.
# set all values to zero ar.fill(0) # display the array print(ar)
Output:
[0 0 0 0 0 0 0]
You can see that each value in the array ar
is now 0
.
The numpy.ndarray.fill()
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, 2, -3], [0, 51, 7], [1, 125, 8]]) # set all values to zero ar.fill(0) # display the array print(ar)
Output:
[[0 0 0] [0 0 0] [0 0 0]]
You can see that each value in the above 2D array is now zero.
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.