numpy.full_like#

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

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.

zeros_like

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

full

Return a new array of given shape filled with value.

Examples

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>>> import numpy as np
>>> x = np.arange(6, dtype=np.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.float64)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
>>> np.full_like(x, np.nan, dtype=np.float64)
array([nan, nan, nan, nan, nan, nan])

>>> y = np.arange(6, dtype=np.float64)
>>> 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=np.int_)
>>> np.full_like(y, [0, 0, 255])
array([[[  0,   0, 255],
        [  0,   0, 255]],
       [[  0,   0, 255],
        [  0,   0, 255]]])

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