numpy.asarray — NumPy v1.11 Manual (original) (raw)
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’. |
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| 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
Similar function which passes through subclasses.
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.
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