This numpy method returns an array of given shape and type as given array, with zeros.
Syntax: numpy.zeros_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-rise 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 zeros having given shape, order and datatype.
Code 1 :
Python
# Python Programming illustrating# numpy.zeros_like methodimport numpy as geekarray = geek.arange(10).reshape(5, 2)print("Original array : \n", array)b = geek.zeros_like(array, float)print("\nMatrix b : \n", b)array = geek.arange(8)c = geek.zeros_like(array)print("\nMatrix c : \n", c) |
Output:
Original array : [[0 1] [2 3] [4 5] [6 7] [8 9]] Matrix b : [[ 0. 0.] [ 0. 0.] [ 0. 0.] [ 0. 0.] [ 0. 0.]] Matrix c : [0 0 0 0 0 0 0 0]
Code 2 :
Python
# Python Programming illustrating# numpy.zeros_like methodimport numpy as geekarray = geek.arange(10).reshape(5, 2)print("Original array : \n", array)array = geek.arange(4).reshape(2, 2)c = geek.zeros_like(array, dtype = 'float')print("\nMatrix : \n", c)array = geek.arange(8)c = geek.zeros_like(array, dtype = 'float', order ='C')print("\nMatrix : \n", c) |
Output :
Original array : [[0 1] [2 3] [4 5] [6 7] [8 9]] Matrix : [[ 0. 0.] [ 0. 0.]] Matrix : [ 0. 0. 0. 0. 0. 0. 0. 0.]
References :
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.zeros_like.html#numpy.zeros_like
Note :
Also, these codes won’t run on online IDE’s. Please run them on your systems to explore the working
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