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

>>> 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=float)
>>> y
array([0., 1., 2.])
>>> np.zeros_like(y)
array([0.,  0.,  0.])