numpy.polynomial.polyutils.as_series¶
-
numpy.polynomial.polyutils.
as_series
(alist, trim=True)[source]¶ Return argument as a list of 1-d arrays.
The returned list contains array(s) of dtype double, complex double, or object. A 1-d argument of shape
(N,)
is parsed intoN
arrays of size one; a 2-d argument of shape(M,N)
is parsed intoM
arrays of sizeN
(i.e., is “parsed by row”); and a higher dimensional array raises a Value Error if it is not first reshaped into either a 1-d or 2-d array.Parameters: alist : array_like
A 1- or 2-d array_like
trim : boolean, optional
When True, trailing zeros are removed from the inputs. When False, the inputs are passed through intact.
Returns: [a1, a2,…] : list of 1-D arrays
A copy of the input data as a list of 1-d arrays.
Raises: ValueError
Raised when
as_series
cannot convert its input to 1-d arrays, or at least one of the resulting arrays is empty.Examples
>>> from numpy.polynomial import polyutils as pu >>> a = np.arange(4) >>> pu.as_series(a) [array([ 0.]), array([ 1.]), array([ 2.]), array([ 3.])] >>> b = np.arange(6).reshape((2,3)) >>> pu.as_series(b) [array([ 0., 1., 2.]), array([ 3., 4., 5.])]
>>> pu.as_series((1, np.arange(3), np.arange(2, dtype=np.float16))) [array([ 1.]), array([ 0., 1., 2.]), array([ 0., 1.])]
>>> pu.as_series([2, [1.1, 0.]]) [array([ 2.]), array([ 1.1])]
>>> pu.as_series([2, [1.1, 0.]], trim=False) [array([ 2.]), array([ 1.1, 0. ])]