dask.array.zeros
dask.array.zeros¶
- dask.array.zeros(*args, **kwargs)¶
Blocked variant of zeros_like
Follows the signature of zeros_like exactly except that it also features optional keyword arguments
chunks: int, tuple, or dict
andname: str
.Original signature follows below.
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.
New in version 1.6.0.
- 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.
New in version 1.6.0.
- 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.
New in version 1.17.0.
- 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.])