numpy.full_like — NumPy v2.2 Manual (original) (raw)
numpy.full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None, *, device=None)[source]#
Return a full array 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.
fill_valuearray_like
Fill value.
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 fill_value with the same shape and type as a.
See also
Return an empty array with shape and type of input.
Return an array of ones with shape and type of input.
Return an array of zeros with shape and type of input.
Return a new array of given shape filled with value.
Examples
import numpy as np x = np.arange(6, dtype=int) np.full_like(x, 1) array([1, 1, 1, 1, 1, 1]) np.full_like(x, 0.1) array([0, 0, 0, 0, 0, 0]) np.full_like(x, 0.1, dtype=np.double) array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) np.full_like(x, np.nan, dtype=np.double) array([nan, nan, nan, nan, nan, nan])
y = np.arange(6, dtype=np.double) np.full_like(y, 0.1) array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
y = np.zeros([2, 2, 3], dtype=int) np.full_like(y, [0, 0, 255]) array([[[ 0, 0, 255], [ 0, 0, 255]], [[ 0, 0, 255], [ 0, 0, 255]]])