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.

在 1.20.0 版新加入.

Returns:

也參考

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.