Numpy zeros_like Function Implementation in Python

Numpy zeros_like Function Implementation in Python with Examples

Sometime you have to create a empty array or zero numpy array while coding. Using the numpy zeros_like method can solve it. What is the use of it ? It allows you to create array of zeros with the same dimension as the dimension of the input array. In this entire tutorial I will show you the implementation of the numpy zeros_like method.

Syntax and Parameter for the Numpy zeros_like

numpy.zeros_like(a, dtype=None, order='K', subok=True, shape=None)[source]

Parameters :

a: Input array

dtype: Type of the output array you want. int or float e.t.c.

order : Overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible.

subok : If True, then the newly created array will use the sub-class type of ‘a’, otherwise it will be a base-class array. Defaults to True.

shape: If it is true then it will overrides the shape of the results.

Examples for the impmentation of Numpy zeros_like

Example 1 : Creating array of zeros for single dimension array.

In this example I will create a single dimentsion numpy array and the find the array of zeros from it. Execute the following code to get the output.

import numpy as np
array_1d = np.arange(15)
np.zeros_like(array_1d)

You will get the output as an arrays of zeros with the same dimension as the input array.

Creating array of zeros for single dimension array
Creating array of zeros for single dimension array

Example 2 : Creating array of zeros for two or multi dimensional array.

Now lets create arrays of zeros for 2 Dimensional array. The method is same. You have to just pass the input array to the numpy.zeros_like() method. Run the below code to implement this example.

import numpy as np
array_2d = np.arange(15).reshape(5,3)
np.zeros_like(array_2d)

Below is the output you will get.

Creating array of zeros for two or multi dimensional array.
Creating array of zeros for two or multi dimensional array.

Where you can use Numpy zeros_like() method ?

You can use zeros_like() method when you want to ouput resultant arrays into it. The use of it won’t allocate or free any memory, which can save you a lot of time. For example I am finding median of the numpy array and outputting the result into arrays of zeros.

import numpy as np
array_2d = np.arange(15).reshape(5,3)
m = np.median(array_2d,axis=0)
dummy = np.zeros_like(m)
np.median(array_2d,axis=0,out=dummy)

Output

"Use

Difference between Numpy zeros and Numpy zeros_like

Some readers has asked me about what is the difference bewtween np.zeros() and np.zeros_like(). The answer is hidden inside the method only.

You have to define the dimension for the numpy.zeros() method. But in case of zeros_like() dimensions is taken from the existing or input array.

 

 

Hope this article has cleared your understanding of zeros_like method. If you have any query then you can contact us for more information.

Source:

Offical Numpy zeros_like() Documentation

Join our list

Subscribe to our mailing list and get interesting stuff and updates to your email inbox.

Thank you for signup. A Confirmation Email has been sent to your Email Address.

Something went wrong.

 
Thank you For sharing.We appreciate your support. Don't Forget to LIKE and FOLLOW our SITE to keep UPDATED with Data Science Learner