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# numpy.asfortranarray

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

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

  Parameters:
    a array_like

      Input array.

    dtype str or dtype object, optional

      By default, the data-type is inferred from the input data.

    like array_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:
    out ndarray

      The input a in Fortran, or column-major, order.

  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.