NumPy: zeros_like() function
zeros_like() function
The zeros_like() function is used to get an array of zeros with the same shape and type as a given array.
Syntax:
numpy.zeros_like(a, dtype=None, order=’K’, subok=True)

Version: 1.15.0
Parameter:
Name | Description | Required / Optional |
---|---|---|
a | The shape and data-type of a define these same attributes of the returned array. | Required |
dtype | Overrides the data type of the result. New in version 1.6.0. | optional |
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. New in version 1.6.0. | optional |
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. | optional |
Return value:
[ndarray] Array of zeros with the same shape and type as a.
Example: numpy.zeros()
>>> import numpy as np
>>> a = np.arange(4)
>>> a = a.reshape((2, 2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.zeros_like(a)
array([[0, 0],
[0, 0]])
Pictorial Presentation:

Example: numpy.zeros() where data type is int
>>> import numpy as np
>>> b = np.arange(5, dtype=float)
>>> b
array([ 0., 1., 2., 3., 4.])
>>> np.zeros_like(b)
array([ 0., 0., 0., 0., 0.])
Pictorial Presentation:

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