numpy.zeros_like#

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

Return an array of zeros with the same shape and type as a given array.

Parameters:

aarray_like

The shape and data-type of a define these same attributes of the returned array.

dtypedata-type, optional

Overrides the data type of the result.

order{C, F, A, or K}, optional

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.

subokbool, optional.

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.

shapeint or sequence of ints, optional.

Overrides the shape of the result. If order=K and the number of dimensions is unchanged, will try to keep order, otherwise, order=C is implied.

devicestr, optional

The device on which to place the created array. Default: None. For Array-API interoperability only, so must be "cpu" if passed.

New in version 2.0.0.

Returns:

outndarray

Array of zeros with the same shape and type as a.

See also

empty_like

Return an empty array with shape and type of input.

ones_like

Return an array of ones with shape and type of input.

full_like

Return a new array with shape of input filled with value.

zeros

Return a new array setting values to zero.

Examples

Try it in your browser!

>>> import numpy as np
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.zeros_like(x)
array([[0, 0, 0],
       [0, 0, 0]])

>>> y = np.arange(3, dtype=np.float64)
>>> y
array([0., 1., 2.])
>>> np.zeros_like(y)
array([0.,  0.,  0.])

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