numpy.asfortranarray#

numpy.asfortranarray(a, dtype=None, *, like=None)#

Return an array (ndim >= 1) laid out in Fortran order in memory.

Parameters:

aarray_like: Input array.
dtypestr or dtype object, optional: By default, the data-type is inferred from the input data.
likearray_like, optional: Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

New in version 1.20.0.

Returns:

See also

ascontiguousarray: Convert input to a contiguous (C order) array.
asanyarray: Convert input to an ndarray with either row or column-major memory order.
require: Return an ndarray that satisfies requirements.
ndarray.flags: Information about the memory layout of the array.

Examples

Starting with a C-contiguous array:

>>> import numpy as np
>>> x = np.ones((2, 3), order='C')
>>> x.flags['C_CONTIGUOUS']
True

Calling asfortranarray makes a Fortran-contiguous copy:

>>> y = np.asfortranarray(x)
>>> y.flags['F_CONTIGUOUS']
True
>>> np.may_share_memory(x, y)
False

Now, starting with a Fortran-contiguous array:

>>> x = np.ones((2, 3), order='F')
>>> x.flags['F_CONTIGUOUS']
True

Then, calling asfortranarray returns the same object:

>>> y = np.asfortranarray(x)
>>> x is y
True

Note: This function returns an array with at least one-dimension (1-d) so it will not preserve 0-d arrays.