numpy.asarray

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

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’}, optional Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to ‘C’.
Returns:	out : ndarray Array interpretation of a. No copy is performed if the input is already an ndarray. 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.matrix, np.ndarray)
True
>>> a = np.matrix([[1, 2]])
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True