Detail: IndexHierarchyGO: Iterator#

IndexHierarchyGO.iter_label(depth_level, /)

iter_label

>>> ih = sf.IndexHierarchyGO.from_product(('a', 'b', 'c'), (1024, 4096, 2048), name='x')
>>> ih
<IndexHierarchyGO: x>
a                     1024
a                     4096
a                     2048
b                     1024
b                     4096
b                     2048
c                     1024
c                     4096
c                     2048
<<U1>                 <int64>
>>> tuple(ih.iter_label())
((np.str_('a'), np.int64(1024)), (np.str_('a'), np.int64(4096)), (np.str_('a'), np.int64(2048)), (np.str_('b'), np.int64(1024)), (np.str_('b'), np.int64(4096)), (np.str_('b'), np.int64(2048)), (np.str_('c'), np.int64(1024)), (np.str_('c'), np.int64(4096)), (np.str_('c'), np.int64(2048)))

IndexHierarchyGO.iter_label(depth_level, /).apply(func, /, *, dtype, name, index_constructor, columns_constructor)

iter_label

IterNodeDelegate.apply(func, /, *, dtype=None, name=None, index_constructor=None, columns_constructor=None)[source]

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.

>>> ih = sf.IndexHierarchyGO.from_product(('a', 'b', 'c'), (1024, 4096, 2048), name='x')
>>> ih
<IndexHierarchyGO: x>
a                     1024
a                     4096
a                     2048
b                     1024
b                     4096
b                     2048
c                     1024
c                     4096
c                     2048
<<U1>                 <int64>
>>> ih.iter_label().apply(lambda l: (l[0].upper(), l[1]))
[('A', np.int64(1024)) ('A', np.int64(4096)) ('A', np.int64(2048))
 ('B', np.int64(1024)) ('B', np.int64(4096)) ('B', np.int64(2048))
 ('C', np.int64(1024)) ('C', np.int64(4096)) ('C', np.int64(2048))]

IndexHierarchyGO.iter_label(depth_level, /).apply_iter(func, /)

iter_label

IterNodeDelegate.apply_iter(func, /)[source]

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

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

>>> ih = sf.IndexHierarchyGO.from_product(('a', 'b', 'c'), (1024, 4096, 2048), name='x')
>>> ih
<IndexHierarchyGO: x>
a                     1024
a                     4096
a                     2048
b                     1024
b                     4096
b                     2048
c                     1024
c                     4096
c                     2048
<<U1>                 <int64>
>>> tuple(ih.iter_label().apply_iter(lambda l: (l[0].upper(), l[1])))
(('A', np.int64(1024)), ('A', np.int64(4096)), ('A', np.int64(2048)), ('B', np.int64(1024)), ('B', np.int64(4096)), ('B', np.int64(2048)), ('C', np.int64(1024)), ('C', np.int64(4096)), ('C', np.int64(2048)))

IndexHierarchyGO.iter_label(depth_level, /).apply_iter_items(func, /)

iter_label

IterNodeDelegate.apply_iter_items(func, /)[source]

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.

>>> ih = sf.IndexHierarchyGO.from_product(('a', 'b', 'c'), (1024, 4096, 2048), name='x')
>>> ih
<IndexHierarchyGO: x>
a                     1024
a                     4096
a                     2048
b                     1024
b                     4096
b                     2048
c                     1024
c                     4096
c                     2048
<<U1>                 <int64>
>>> tuple(ih.iter_label().apply_iter_items(lambda l: (l[0].upper(), l[1])))
((0, ('A', np.int64(1024))), (1, ('A', np.int64(4096))), (2, ('A', np.int64(2048))), (3, ('B', np.int64(1024))), (4, ('B', np.int64(4096))), (5, ('B', np.int64(2048))), (6, ('C', np.int64(1024))), (7, ('C', np.int64(4096))), (8, ('C', np.int64(2048))))

IndexHierarchyGO.iter_label(depth_level, /).apply_pool(func, /, *, dtype, name, index_constructor, max_workers, chunksize, use_threads)

iter_label

IterNodeDelegate.apply_pool(func, /, *, dtype=None, name=None, index_constructor=None, max_workers=None, chunksize=1, use_threads=False)[source]

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.

>>> ih = sf.IndexHierarchyGO.from_product(('a', 'b', 'c'), (1024, 4096, 2048), name='x')
>>> ih
<IndexHierarchyGO: x>
a                     1024
a                     4096
a                     2048
b                     1024
b                     4096
b                     2048
c                     1024
c                     4096
c                     2048
<<U1>                 <int64>
>>> ih.iter_label().apply_pool(lambda l: (l[0].upper(), l[1]), use_threads=True)
[('A', np.int64(1024)) ('A', np.int64(4096)) ('A', np.int64(2048))
 ('B', np.int64(1024)) ('B', np.int64(4096)) ('B', np.int64(2048))
 ('C', np.int64(1024)) ('C', np.int64(4096)) ('C', np.int64(2048))]