bundles / numpy 2.4.4 / numpy / full_like
_ArrayFunctionDispatcher
numpy:full_like
source: /numpy/_core/numeric.py :400
Signature
def full_like ( a , fill_value , dtype = None , order = K , subok = True , shape = None , * , device = None ) Summary
Return a full array with the same shape and type as a given array.
Parameters
a: array_likeThe shape and data-type of
adefine these same attributes of the returned array.fill_value: array_likeFill value.
dtype: data-type, optionalOverrides the data type of the result.
order: {'C', 'F', 'A', or 'K'}, optionalOverrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if
ais Fortran contiguous, 'C' otherwise. 'K' means match the layout ofaas closely as possible.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.
device: str, optionalThe device on which to place the created array. Default: None. For Array-API interoperability only, so must be
"cpu"if passed.
Returns
out: ndarrayArray of
fill_valuewith the same shape and type asa.
Examples
import numpy as np x = np.arange(6, dtype=int) np.full_like(x, 1) np.full_like(x, 0.1) np.full_like(x, 0.1, dtype=np.double) np.full_like(x, np.nan, dtype=np.double)✓
y = np.arange(6, dtype=np.double) np.full_like(y, 0.1)✓
y = np.zeros([2, 2, 3], dtype=int)
✓np.full_like(y, [0, 0, 255])
✗See also
- empty_like
Return an empty array with shape and type of input.
- full
Return a new array of given shape filled with value.
- ones_like
Return an array of ones with shape and type of input.
- zeros_like
Return an array of zeros with shape and type of input.
Aliases
-
numpy.full_like