Detail: Series: Accessor Values¶

Series.via_values.apply(func, *args, **kwargs)

Series.via_values: Interface for applying functions to values (as arrays) in this container.

InterfaceValues.apply(func, *args, **kwargs)[source]

>>> s = sf.Series((10, 2, 8), index=('a', 'b', 'c'))
>>> s.via_values.apply(np.sin)
<Series>
<Index>
a        -0.5440211108893698
b        0.9092974268256817
c        0.9893582466233818
<<U1>    <float64>

Series.via_values.__array_ufunc__(ufunc, method, *args, **kwargs)

Series.via_values: Interface for applying functions to values (as arrays) in this container.

InterfaceValues.__array_ufunc__(ufunc, method, *args, **kwargs)[source]: Support for applying NumPy functions directly on containers, returning NumPy arrays.

>>> s = sf.Series((10, 2, 8), index=('a', 'b', 'c'))
>>> np.sin(s.via_values)
<Series>
<Index>
a        -0.5440211108893698
b        0.9092974268256817
c        0.9893582466233818
<<U1>    <float64>

Series.via_values.__call__(*, consolidate_blocks, unify_blocks, dtype)

Series.via_values: Interface for applying functions to values (as arrays) in this container.

InterfaceValues.__call__(*, consolidate_blocks=False, unify_blocks=False, dtype=None)[source]

Parameters:

consolidate_blocks – Group adjacent same-typed arrays into 2D arrays.
unify_blocks – Group all arrays into single array, re-typing to an appropriate dtype.
dtype – specify a dtype to be used in conversion before consolidation or unification, and before function application.

>>> s = sf.Series((10, 2, 8), index=('a', 'b', 'c'))
>>> np.sin(s.via_values(unify_blocks=True))
<Series>
<Index>
a        -0.5440211108893698
b        0.9092974268256817
c        0.9893582466233818
<<U1>    <float64>