Detail: IndexHierarchyGO: Iterator

IndexHierarchyGO.iter_label(depth_level)

iter_label

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