Numpy.zeros_like — NumPy V1.23 Manual

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• numpy.zeros_like

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! >>> importnumpyasnp >>> 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.]) Go BackOpen In Tab On this page

• zeros_like