numpy.asanyarray#
- numpy.asanyarray(a, dtype=None, order=None, *, like=None)#
Convert the input to an ndarray, but pass ndarray subclasses through.
- Parameters:
- aarray_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.
- 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.
- Returns:
- 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
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] >>> 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