Detail: Index: Iterator

Index.iter_label(depth_level)

iter_label

>>> ix = sf.Index(('a', 'b', 'c', 'd', 'e'), name='x')
>>> ix
<Index: x>
a
b
c
d
e
<<U1>
>>> tuple(ix.iter_label())
('a', 'b', 'c', 'd', 'e')

Index.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.

>>> ix = sf.Index(('a', 'b', 'c', 'd', 'e'), name='x')
>>> ix
<Index: x>
a
b
c
d
e
<<U1>
>>> ix.iter_label().apply(lambda l: l if l == 'b' else l.upper())
['A' 'b' 'C' 'D' 'E']

Index.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.

>>> ix = sf.Index(('a', 'b', 'c', 'd', 'e'), name='x')
>>> ix
<Index: x>
a
b
c
d
e
<<U1>
>>> tuple(ix.iter_label().apply_iter(lambda l: l if l == 'b' else l.upper()))
('A', 'b', 'C', 'D', 'E')

Index.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.

>>> ix = sf.Index(('a', 'b', 'c', 'd', 'e'), name='x')
>>> ix
<Index: x>
a
b
c
d
e
<<U1>
>>> tuple(ix.iter_label().apply_iter_items(lambda l: l if l == 'b' else l.upper()))
((0, 'A'), (1, 'b'), (2, 'C'), (3, 'D'), (4, 'E'))

Index.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.

>>> ix = sf.Index(('a', 'b', 'c', 'd', 'e'), name='x')
>>> ix
<Index: x>
a
b
c
d
e
<<U1>
>>> ix.iter_label().apply_pool(lambda l: l if l == 'b' else l.upper(), use_threads=True)
['A' 'b' 'C' 'D' 'E']