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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. Ifa
is a subclass of ndarray, a base class ndarray is returned.See also
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asanyarray
- Similar function which passes through subclasses.
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ascontiguousarray
- Convert input to a contiguous array.
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asfarray
- Convert input to a floating point ndarray.
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asfortranarray
- Convert input to an ndarray with column-major memory order.
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asarray_chkfinite
- Similar function which checks input for NaNs and Infs.
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fromiter
- Create an array from an iterator.
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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
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numpy.asarray()
2017-01-10 18:12:51
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