# numpy.asanyarray

numpy.asanyarray(a, dtype=None, order=None, *, like=None)

  Convert the input to an ndarray, but pass ndarray subclasses through.

  Parameters:
    a array_like

      Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays.

    dtype data-type, optional

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

    order{‘C’, ‘F’, ‘A’, ‘K’}, optional

      Memory layout. ‘A’ and ‘K’ depend on the order of input array a. ‘C’ row-major (C-style), ‘F’ column-major (Fortran-style) memory representation. ‘A’ (any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise ‘K’ (keep) preserve input order Defaults to ‘C’.

    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 or an ndarray subclass

      Array interpretation of a. If a is an ndarray or a subclass of ndarray, it is returned as-is and no copy is performed.

  See also

  `asarray`

    Similar function which always returns ndarrays.

  `ascontiguousarray`

    Convert input to a contiguous array.

  `asfarray`

    Convert input to a floating point ndarray.

  `asfortranarray`

    Convert input to an ndarray with column-major memory order.

  `asarray_chkfinite`

    Similar function which checks input for NaNs and Infs.

  `fromiter`

    Create an array from an iterator.

  `fromfunction`

    Construct an array by executing a function on grid positions.

  Examples

  Convert a list into an array:

      >>> a = [1, 2]
      >>> np.asanyarray(a)
      array([1, 2])

  Instances of `ndarray` subclasses are passed through as-is:

      >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
      >>> np.asanyarray(a) is a
      True