Detail: Bus: Iterator#

Bus.iter_element

iter_element: Iterator of elements.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element())
(<Frame: x>
<Index>    a       b       <<U1>
<Index>
p          0       1
q          2       3
r          4       5
<<U1>      <int64> <int64>, <Frame: y>
<Index>    c      d      <<U1>
<Index>
p          False  True
q          False  True
r          False  True
<<U1>      <bool> <bool>, <Frame: v>
<Index>    a       b       <<U1>
<Index>
p          40      41
q          42      43
r          44      45
<<U1>      <int64> <int64>, <Frame: w>
<Index>    c      d      <<U1>
<Index>
p          False  True
q          True   False
r          True   True
<<U1>      <bool> <bool>)

Bus.iter_element().apply(func, /, *, dtype, name, index_constructor, columns_constructor)

iter_element: Iterator of elements.

IterNodeDelegateReducible.apply(func, /, *, dtype=None, name=None, index_constructor=None, columns_constructor=None)

Apply a function to each value. Returns a new container.

Parameters:

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element().apply(lambda f: f.shape)
<Series>
<Index>
x        (3, 2)
y        (3, 2)
v        (3, 2)
w        (3, 2)
<<U1>    <object>

Bus.iter_element().apply_iter(func, /)

iter_element: Iterator of elements.

IterNodeDelegateReducible.apply_iter(func, /)

Apply a function to each value. A generator of resulting values.

Parameters:: func – A function that takes a value.
Yields:: Values after function application.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().apply_iter(lambda f: f.nbytes))
(48, 6, 48, 6)

Bus.iter_element().apply_iter_items(func, /)

iter_element: Iterator of elements.

IterNodeDelegateReducible.apply_iter_items(func, /)

Apply a function to each value. A generator of resulting key, value pairs.

Parameters:: func – A function that takes a value.
Yields:: Pairs of label, value after function application.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().apply_iter_items(lambda f: f.nbytes))
((np.str_('x'), 48), (np.str_('y'), 6), (np.str_('v'), 48), (np.str_('w'), 6))

Bus.iter_element().apply_pool(func, /, *, dtype, name, index_constructor, max_workers, chunksize, use_threads)

iter_element: Iterator of elements.

IterNodeDelegateReducible.apply_pool(func, /, *, dtype=None, name=None, index_constructor=None, max_workers=None, chunksize=1, use_threads=False)

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters:

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> def func(f): return f.sum().sum()
>>> b.iter_element().apply_pool(func, use_threads=True)
<Series>
<Index>
x        15
y        3
v        255
w        4
<<U1>    <int64>

Bus.iter_element().reduce.from_func(func, *, fill_value).keys()

iter_element: Iterator of elements.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_func(lambda f: f.iloc[1:]).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_func(func, *, fill_value).__iter__()

iter_element: Iterator of elements.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_func(lambda f: f.iloc[1:]).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_func(func, *, fill_value).items()

iter_element: Iterator of elements.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_func(lambda f: f.iloc[1:]).items())
((np.str_('x'), <Frame: x>
<Index>    a       b       <<U1>
<Index>
q          2       3
r          4       5
<<U1>      <int64> <int64>), (np.str_('y'), <Frame: y>
<Index>    c      d      <<U1>
<Index>
q          False  True
r          False  True
<<U1>      <bool> <bool>), (np.str_('v'), <Frame: v>
<Index>    a       b       <<U1>
<Index>
q          42      43
r          44      45
<<U1>      <int64> <int64>), (np.str_('w'), <Frame: w>
<Index>    c      d      <<U1>
<Index>
q          True   False
r          True   True
<<U1>      <bool> <bool>))

Bus.iter_element().reduce.from_func(func, *, fill_value).values()

iter_element: Iterator of elements.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_func(lambda f: f.iloc[1:]).values())
(<Frame: x>
<Index>    a       b       <<U1>
<Index>
q          2       3
r          4       5
<<U1>      <int64> <int64>, <Frame: y>
<Index>    c      d      <<U1>
<Index>
q          False  True
r          False  True
<<U1>      <bool> <bool>, <Frame: v>
<Index>    a       b       <<U1>
<Index>
q          42      43
r          44      45
<<U1>      <int64> <int64>, <Frame: w>
<Index>    c      d      <<U1>
<Index>
q          True   False
r          True   True
<<U1>      <bool> <bool>)

Bus.iter_element().reduce.from_func(func, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element: Iterator of elements.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element().reduce.from_func(lambda f: f.iloc[1:]).to_frame(index=sf.IndexAutoFactory)
<Frame>
<Index> a         b         c        d        <<U1>
<Index>
0       2.0       3.0       nan      nan
1       4.0       5.0       nan      nan
2       nan       nan       False    True
3       nan       nan       False    True
4       42.0      43.0      nan      nan
5       44.0      45.0      nan      nan
6       nan       nan       True     False
7       nan       nan       True     True
<int64> <float64> <float64> <object> <object>

Bus.iter_element().reduce.from_map_func(func, *, fill_value).keys()

iter_element: Iterator of elements.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_map_func(np.min).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_map_func(func, *, fill_value).__iter__()

iter_element: Iterator of elements.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_map_func(np.min).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_map_func(func, *, fill_value).items()

iter_element: Iterator of elements.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_map_func(np.min).items())
((np.str_('x'), <Series: x>
<Index>
a           0
b           1
<<U1>       <int64>), (np.str_('y'), <Series: y>
<Index>
c           False
d           True
<<U1>       <bool>), (np.str_('v'), <Series: v>
<Index>
a           40
b           41
<<U1>       <int64>), (np.str_('w'), <Series: w>
<Index>
c           False
d           False
<<U1>       <bool>))

Bus.iter_element().reduce.from_map_func(func, *, fill_value).values()

iter_element: Iterator of elements.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_map_func(np.min).values())
(<Series: x>
<Index>
a           0
b           1
<<U1>       <int64>, <Series: y>
<Index>
c           False
d           True
<<U1>       <bool>, <Series: v>
<Index>
a           40
b           41
<<U1>       <int64>, <Series: w>
<Index>
c           False
d           False
<<U1>       <bool>)

Bus.iter_element().reduce.from_map_func(func, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element: Iterator of elements.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element().reduce.from_map_func(np.min).to_frame()
<Frame>
<Index> a         b         c        d        <<U1>
<Index>
x       0.0       1.0       nan      nan
y       nan       nan       False    True
v       40.0      41.0      nan      nan
w       nan       nan       False    False
<<U1>   <float64> <float64> <object> <object>

Bus.iter_element().reduce.from_label_map(func_map, *, fill_value).keys()

iter_element: Iterator of elements.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_map({'b': np.min, 'a': np.max}).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_label_map(func_map, *, fill_value).__iter__()

iter_element: Iterator of elements.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_map({'b': np.min, 'a': np.max}).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_label_map(func_map, *, fill_value).items()

iter_element: Iterator of elements.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_map({'b': np.min, 'a': np.max}).items())
((np.str_('x'), <Series: x>
<Index>
b           1
a           4
<<U1>       <int64>), (np.str_('y'), <Series: y>
<Index>
b           nan
a           nan
<<U1>       <float64>), (np.str_('v'), <Series: v>
<Index>
b           41
a           44
<<U1>       <int64>), (np.str_('w'), <Series: w>
<Index>
b           nan
a           nan
<<U1>       <float64>))

Bus.iter_element().reduce.from_label_map(func_map, *, fill_value).values()

iter_element: Iterator of elements.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_map({'b': np.min, 'a': np.max}).values())
(<Series: x>
<Index>
b           1
a           4
<<U1>       <int64>, <Series: y>
<Index>
b           nan
a           nan
<<U1>       <float64>, <Series: v>
<Index>
b           41
a           44
<<U1>       <int64>, <Series: w>
<Index>
b           nan
a           nan
<<U1>       <float64>)

Bus.iter_element().reduce.from_label_map(func_map, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element: Iterator of elements.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element().reduce.from_label_map({'b': np.min, 'a': np.max}).to_frame()
<Frame>
<Index> b         a         <<U1>
<Index>
x       1.0       4.0
y       nan       nan
v       41.0      44.0
w       nan       nan
<<U1>   <float64> <float64>

Bus.iter_element().reduce.from_label_pair_map(func_map, *, fill_value).keys()

iter_element: Iterator of elements.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_label_pair_map(func_map, *, fill_value).__iter__()

iter_element: Iterator of elements.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element().reduce.from_label_pair_map(func_map, *, fill_value).items()

iter_element: Iterator of elements.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).items())
((np.str_('x'), <Series: x>
<Index>
b-min       1
b-max       5
<<U5>       <int64>), (np.str_('y'), <Series: y>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>), (np.str_('v'), <Series: v>
<Index>
b-min       41
b-max       45
<<U5>       <int64>), (np.str_('w'), <Series: w>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>))

Bus.iter_element().reduce.from_label_pair_map(func_map, *, fill_value).values()

iter_element: Iterator of elements.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element().reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).values())
(<Series: x>
<Index>
b-min       1
b-max       5
<<U5>       <int64>, <Series: y>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>, <Series: v>
<Index>
b-min       41
b-max       45
<<U5>       <int64>, <Series: w>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>)

Bus.iter_element().reduce.from_label_pair_map(func_map, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element: Iterator of elements.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element().reduce.from_label_pair_map({('b', 'b-min'): np.min, ('b', 'b-max'): np.max}).to_frame()
<Frame>
<Index> b-min     b-max     <<U5>
<Index>
x       1.0       5.0
y       nan       nan
v       41.0      45.0
w       nan       nan
<<U1>   <float64> <float64>

Bus.iter_element_items

iter_element_items: Iterator of label, element pairs.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items())
((np.str_('x'), <Frame: x>
<Index>    a       b       <<U1>
<Index>
p          0       1
q          2       3
r          4       5
<<U1>      <int64> <int64>), (np.str_('y'), <Frame: y>
<Index>    c      d      <<U1>
<Index>
p          False  True
q          False  True
r          False  True
<<U1>      <bool> <bool>), (np.str_('v'), <Frame: v>
<Index>    a       b       <<U1>
<Index>
p          40      41
q          42      43
r          44      45
<<U1>      <int64> <int64>), (np.str_('w'), <Frame: w>
<Index>    c      d      <<U1>
<Index>
p          False  True
q          True   False
r          True   True
<<U1>      <bool> <bool>))

Bus.iter_element_items().apply(func, /, *, dtype, name, index_constructor, columns_constructor)

iter_element_items: Iterator of label, element pairs.

IterNodeDelegateReducible.apply(func, /, *, dtype=None, name=None, index_constructor=None, columns_constructor=None)

Apply a function to each value. Returns a new container.

Parameters:

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element_items().apply(lambda l, f: f.size if l != 'v' else 0)
<Series>
<Index>
x        6
y        6
v        0
w        6
<<U1>    <int64>

Bus.iter_element_items().apply_iter(func, /)

iter_element_items: Iterator of label, element pairs.

IterNodeDelegateReducible.apply_iter(func, /)

Apply a function to each value. A generator of resulting values.

Parameters:: func – A function that takes a value.
Yields:: Values after function application.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().apply_iter(lambda l, f: f.shape if l != 'x' else 0))
(0, (3, 2), (3, 2), (3, 2))

Bus.iter_element_items().apply_iter_items(func, /)

iter_element_items: Iterator of label, element pairs.

IterNodeDelegateReducible.apply_iter_items(func, /)

Apply a function to each value. A generator of resulting key, value pairs.

Parameters:: func – A function that takes a value.
Yields:: Pairs of label, value after function application.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().apply_iter_items(lambda l, f: f.shape if l != 'x' else 0))
((np.str_('x'), 0), (np.str_('y'), (3, 2)), (np.str_('v'), (3, 2)), (np.str_('w'), (3, 2)))

Bus.iter_element_items().apply_pool(func, /, *, dtype, name, index_constructor, max_workers, chunksize, use_threads)

iter_element_items: Iterator of label, element pairs.

IterNodeDelegateReducible.apply_pool(func, /, *, dtype=None, name=None, index_constructor=None, max_workers=None, chunksize=1, use_threads=False)

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters:

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> def func(pair): return pair[1].sum().sum() if pair[0] != 'v' else -1
>>> b.iter_element_items().apply_pool(func, use_threads=True)
<Series>
<Index>
x        15
y        3
v        -1
w        4
<<U1>    <int64>

Bus.iter_element_items().reduce.from_func(func, *, fill_value).keys()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_func(lambda l, f: f.iloc[1:]).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_func(func, *, fill_value).__iter__()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_func(lambda l, f: f.iloc[1:]).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_func(func, *, fill_value).items()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_func(lambda l, f: f.iloc[1:]).items())
((np.str_('x'), <Frame: x>
<Index>    a       b       <<U1>
<Index>
q          2       3
r          4       5
<<U1>      <int64> <int64>), (np.str_('y'), <Frame: y>
<Index>    c      d      <<U1>
<Index>
q          False  True
r          False  True
<<U1>      <bool> <bool>), (np.str_('v'), <Frame: v>
<Index>    a       b       <<U1>
<Index>
q          42      43
r          44      45
<<U1>      <int64> <int64>), (np.str_('w'), <Frame: w>
<Index>    c      d      <<U1>
<Index>
q          True   False
r          True   True
<<U1>      <bool> <bool>))

Bus.iter_element_items().reduce.from_func(func, *, fill_value).values()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_func(lambda l, f: f.iloc[1:]).values())
(<Frame: x>
<Index>    a       b       <<U1>
<Index>
q          2       3
r          4       5
<<U1>      <int64> <int64>, <Frame: y>
<Index>    c      d      <<U1>
<Index>
q          False  True
r          False  True
<<U1>      <bool> <bool>, <Frame: v>
<Index>    a       b       <<U1>
<Index>
q          42      43
r          44      45
<<U1>      <int64> <int64>, <Frame: w>
<Index>    c      d      <<U1>
<Index>
q          True   False
r          True   True
<<U1>      <bool> <bool>)

Bus.iter_element_items().reduce.from_func(func, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_func(func, *, fill_value=nan)[source]: For each Frame, and given a function func that returns either a Series or a Frame, call that function on each Frame.

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element_items().reduce.from_func(lambda l, f: f.iloc[1:]).to_frame(index=sf.IndexAutoFactory)
<Frame>
<Index> a         b         c        d        <<U1>
<Index>
0       2.0       3.0       nan      nan
1       4.0       5.0       nan      nan
2       nan       nan       False    True
3       nan       nan       False    True
4       42.0      43.0      nan      nan
5       44.0      45.0      nan      nan
6       nan       nan       True     False
7       nan       nan       True     True
<int64> <float64> <float64> <object> <object>

Bus.iter_element_items().reduce.from_map_func(func, *, fill_value).keys()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_map_func(lambda s: np.min(s)).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_map_func(func, *, fill_value).__iter__()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_map_func(lambda s: np.min(s)).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_map_func(func, *, fill_value).items()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_map_func(lambda s: np.min(s)).items())
((np.str_('x'), <Series: x>
<Index>
a           0
b           1
<<U1>       <int64>), (np.str_('y'), <Series: y>
<Index>
c           False
d           True
<<U1>       <bool>), (np.str_('v'), <Series: v>
<Index>
a           40
b           41
<<U1>       <int64>), (np.str_('w'), <Series: w>
<Index>
c           False
d           False
<<U1>       <bool>))

Bus.iter_element_items().reduce.from_map_func(func, *, fill_value).values()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_map_func(lambda s: np.min(s)).values())
(<Series: x>
<Index>
a           0
b           1
<<U1>       <int64>, <Series: y>
<Index>
c           False
d           True
<<U1>       <bool>, <Series: v>
<Index>
a           40
b           41
<<U1>       <int64>, <Series: w>
<Index>
c           False
d           False
<<U1>       <bool>)

Bus.iter_element_items().reduce.from_map_func(func, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_map_func(func, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element_items().reduce.from_map_func(lambda s: np.min(s)).to_frame()
<Frame>
<Index> a         b         c        d        <<U1>
<Index>
x       0.0       1.0       nan      nan
y       nan       nan       False    True
v       40.0      41.0      nan      nan
w       nan       nan       False    False
<<U1>   <float64> <float64> <object> <object>

Bus.iter_element_items().reduce.from_label_map(func_map, *, fill_value).keys()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_map({'b': lambda l, s: np.min(s), 'a': lambda l, s: np.max(s)}).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_label_map(func_map, *, fill_value).__iter__()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_map({'b': lambda l, s: np.min(s), 'a': lambda l, s: np.max(s)}).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_label_map(func_map, *, fill_value).items()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_map({'b': lambda l, s: np.min(s), 'a': lambda l, s: np.max(s)}).items())
((np.str_('x'), <Series: x>
<Index>
b           1
a           4
<<U1>       <int64>), (np.str_('y'), <Series: y>
<Index>
b           nan
a           nan
<<U1>       <float64>), (np.str_('v'), <Series: v>
<Index>
b           41
a           44
<<U1>       <int64>), (np.str_('w'), <Series: w>
<Index>
b           nan
a           nan
<<U1>       <float64>))

Bus.iter_element_items().reduce.from_label_map(func_map, *, fill_value).values()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_map({'b': lambda l, s: np.min(s), 'a': lambda l, s: np.max(s)}).values())
(<Series: x>
<Index>
b           1
a           4
<<U1>       <int64>, <Series: y>
<Index>
b           nan
a           nan
<<U1>       <float64>, <Series: v>
<Index>
b           41
a           44
<<U1>       <int64>, <Series: w>
<Index>
b           nan
a           nan
<<U1>       <float64>)

Bus.iter_element_items().reduce.from_label_map(func_map, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element_items().reduce.from_label_map({'b': lambda l, s: np.min(s), 'a': lambda l, s: np.max(s)}).to_frame()
<Frame>
<Index> b         a         <<U1>
<Index>
x       1.0       4.0
y       nan       nan
v       41.0      44.0
w       nan       nan
<<U1>   <float64> <float64>

Bus.iter_element_items().reduce.from_label_pair_map(func_map, *, fill_value).keys()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_pair_map({('b', 'b-min'): lambda l, s: np.min(s), ('b', 'b-max'): lambda l, s: np.max(s)}).keys())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_label_pair_map(func_map, *, fill_value).__iter__()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_pair_map({('b', 'b-min'): lambda l, s: np.min(s), ('b', 'b-max'): lambda l, s: np.max(s)}).__iter__())
(np.str_('x'), np.str_('y'), np.str_('v'), np.str_('w'))

Bus.iter_element_items().reduce.from_label_pair_map(func_map, *, fill_value).items()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_pair_map({('b', 'b-min'): lambda l, s: np.min(s), ('b', 'b-max'): lambda l, s: np.max(s)}).items())
((np.str_('x'), <Series: x>
<Index>
b-min       1
b-max       5
<<U5>       <int64>), (np.str_('y'), <Series: y>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>), (np.str_('v'), <Series: v>
<Index>
b-min       41
b-max       45
<<U5>       <int64>), (np.str_('w'), <Series: w>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>))

Bus.iter_element_items().reduce.from_label_pair_map(func_map, *, fill_value).values()

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> tuple(b.iter_element_items().reduce.from_label_pair_map({('b', 'b-min'): lambda l, s: np.min(s), ('b', 'b-max'): lambda l, s: np.max(s)}).values())
(<Series: x>
<Index>
b-min       1
b-max       5
<<U5>       <int64>, <Series: y>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>, <Series: v>
<Index>
b-min       41
b-max       45
<<U5>       <int64>, <Series: w>
<Index>
b-min       nan
b-max       nan
<<U5>       <float64>)

Bus.iter_element_items().reduce.from_label_pair_map(func_map, *, fill_value).to_frame(*, index, columns, index_constructor, columns_constructor, name, consolidate_blocks)

iter_element_items: Iterator of label, element pairs.

ReduceDispatch.from_label_pair_map(func_map, *, fill_value=nan)[source]

>>> b = sf.Bus.from_frames((sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x'), sf.Frame((np.arange(6).reshape(3,2) % 2).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='y'), sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'), sf.Frame((np.arange(6).reshape(3,2) % 3).astype(bool), index=('p', 'q', 'r'), columns=('c', 'd'), name='w')), name='k')
>>> b
<Bus: k>
<Index>
x        Frame
y        Frame
v        Frame
w        Frame
<<U1>    <object>
>>> b.iter_element_items().reduce.from_label_pair_map({('b', 'b-min'): lambda l, s: np.min(s), ('b', 'b-max'): lambda l, s: np.max(s)}).to_frame()
<Frame>
<Index> b-min     b-max     <<U5>
<Index>
x       1.0       5.0
y       nan       nan
v       41.0      45.0
w       nan       nan
<<U1>   <float64> <float64>