Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
numpy.ones_like() in Python
The numpy.one_like() function returns an array of given shape and type as a given array, with ones.
Syntax: numpy.ones_like(array, dtype = None, order = 'K', subok = True)
Parameters :
array : array_like input
subok : [optional, boolean]If true, then newly created array will be sub-class of array;
otherwise, a base-class array
order : C_contiguous or F_contiguous
C-contiguous order in memory(last index varies the fastest)
C order means that operating row-wise on the array will be slightly quicker
FORTRAN-contiguous order in memory (first index varies the fastest).
F order means that column-wise operations will be faster.
dtype : [optional, float(byDefault)] Data type of returned array.
Returns :
ndarray of ones having given shape, order and datatype.
# Python Programming illustrating# numpy.ones_like method import numpy as geek array = geek.arange(10).reshape(5, 2)print("Original array : \n", array) b = geek.ones_like(array, float)print("\nMatrix b : \n", b) array = geek.arange(8)c = geek.ones_like(array)print("\nMatrix c : \n", c) |
Output:
Original array : [[0 1] [2 3] [4 5] [6 7] [8 9]] Matrix b : [[ 1. 1.] [ 1. 1.] [ 1. 1.] [ 1. 1.] [ 1. 1.]] Matrix c : [1 1 1 1 1 1 1 1]
References :
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.ones_like.html
Note :
Also, these codes won’t run on online-ID. Please run them on your systems to explore the working
This article is contributed by Mohit Gupta_OMG 😀. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course
