zeros_like(a, dtype=None, order='K', subok=True, shape=None)

Parameters

a : array_like

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

dtype : data-type, optional

Overrides the data type of the result.

versionadded

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.

versionadded

subok : bool, 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.

shape : int 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.

versionadded

Returns

out : ndarray

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

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

See Also

empty_like

Return an empty array with shape and type of input.

full_like

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

ones_like

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

zeros

Return a new array setting values to zero.

Examples

>>> 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.])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.core._multiarray_umath.empty_like skimage.feature.orb.ORB skimage.segmentation.random_walker_segmentation.random_walker dask.array.reductions.median numpy.zeros numpy.empty_like numpy.full_like skimage.measure.profile.profile_line scipy.signal._peak_finding.find_peaks skimage.restoration.inpaint.inpaint_biharmonic numpy.ones_like numpy.array

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

GitHub : /numpy/core/numeric.py#76
type: <class 'function'>
Commit: