xorbits.numpy.asarray#
- xorbits.numpy.asarray(a, dtype=None, order=None, *, like=None)[源代码]#
Convert the input to an array.
- 参数
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', '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 ‘K’.
like (array_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.1.20.0(numpy) 新版功能.
- 返回
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.
- 返回类型
ndarray
参见
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.
实际案例
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
This docstring was copied from numpy.