full(shape, fill_value, *args, **kwargs)

Follows the signature of full_like exactly except that it also features optional keyword arguments chunks: int, tuple, or dict and name: str .

Original signature follows below.

Return a full array with the same shape and type as a given array.

Parameters

a : array_like

The shape and data-type of a define these same attributes of the returned array.

fill_value : scalar

Fill value.

dtype : data-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.

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.

versionadded

Returns

out : ndarray

Array of :None:None:`fill_value` with the same shape and type as a.

Blocked variant of full_like

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.

Examples

This example is valid syntax, but we were not able to check execution

>>> x = np.arange(6, dtype=int)
... np.full_like(x, 1)
array([1, 1, 1, 1, 1, 1])

This example is valid syntax, but we were not able to check execution

>>> np.full_like(x, 0.1)
array([0, 0, 0, 0, 0, 0])

This example is valid syntax, but we were not able to check execution

>>> np.full_like(x, 0.1, dtype=np.double)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])

This example is valid syntax, but we were not able to check execution

>>> np.full_like(x, np.nan, dtype=np.double)
array([nan, nan, nan, nan, nan, nan])

This example is valid syntax, but we were not able to check execution

>>> 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])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.routines.array dask.array.creation.full_like dask.array.wrap.full dask.utils.empty

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