# numpy.asarray

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

  Convert the input to an array.

  Parameters
    a array_like

      Input data, in any form that can be converted to an array. This includes 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 ‘K’.

    like array_like

      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

      Array interpretation of a. No copy is performed if the input is already an ndarray with matching dtype and order. If a is a subclass of ndarray, a base class ndarray is returned.

  See also

  `asanyarray`

    Similar function which passes through subclasses.

  `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.asarray(a)
      array([1, 2])

  Existing arrays are not copied:

      >>> a = np.array([1, 2])
      >>> np.asarray(a) is a
      True

  If `dtype` is set, array is copied only if dtype does not match:

      >>> a = np.array([1, 2], dtype=np.float32)
      >>> np.asarray(a, dtype=np.float32) is a
      True
      >>> np.asarray(a, dtype=np.float64) is a
      False

  Contrary to `asanyarray`, ndarray subclasses are not passed through:

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

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