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
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