jax.numpy.asarray¶

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

Convert the input to an array.

LAX-backend implementation of asarray(). Original docstring below.

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

Returns

out – 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.

Return type

ndarray

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