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

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


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.

dtypedata-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’.

likearray_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.

outndarray 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


Similar function which always returns ndarrays.


Convert input to a contiguous array.


Convert input to a floating point ndarray.


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


Similar function which checks input for NaNs and Infs.


Create an array from an iterator.


Construct an array by executing a function on grid positions.


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