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# numpy.asanyarray

numpy.asanyarray(a, dtype=None, order=None, *, device=None, copy=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

      The memory layout of the output. ‘C’ gives a row-major layout (C-style), ‘F’ gives a column-major layout (Fortran-style). ‘C’ and ‘F’ will copy if needed to ensure the output format. ‘A’ (any) is equivalent to ‘F’ if input a is non-contiguous or Fortran-contiguous, otherwise, it is equivalent to ‘C’. Unlike ‘C’ or ‘F’, ‘A’ does not ensure that the result is contiguous. ‘K’ (keep) preserves the input order for the output. ‘C’ is the default.

    device str, optional

      The device on which to place the created array. Default: `None`. For Array-API interoperability only, so must be `"cpu"` if passed.

      New in version 2.1.0.

    copy bool, optional

      If `True`, then the object is copied. If `None` then the object is copied only if needed, i.e. if `__array__` returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (`dtype`, `order`, etc.). For `False` it raises a `ValueError` if a copy cannot be avoided. Default: `None`.

      New in version 2.1.0.

    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.

  `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]
      >>> import numpy as np
      >>> np.asanyarray(a)
      array([1, 2])

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

      >>> a = np.array([(1., 2), (3., 4)], dtype='f4,i4').view(np.recarray)
      >>> np.asanyarray(a) is a
      True