numpy.full_like¶

numpy.full_like(a, fill_value, dtype=None, order='K', subok=True, shape=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_valuescalar: 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.

New in version 1.17.0.

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

>>> 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])

numpy.full

numpy.array