numpy.asanyarray¶

numpy.asanyarray(a, dtype=None, order=None)[source]¶

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

Parameters:

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’}, optional Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to ‘C’.
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.

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’}, optional: Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to ‘C’.

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