This is documentation for an old release of NumPy (version 1.12.0). Read this page in the documentation of the latest stable release (version > 1.17).
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 with matching dtype and order. 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