ubelt.util_list module

Utility functions for manipulating iterables, lists, and sequences.

The chunks() function splits a list into smaller parts. There are different strategies for how to do this.

The flatten() function take a list of lists and removees the inner lists. This only removes one level of nesting.

The iterable() function checks if an object is iterable or not. Similar to the callable() builtin function.

The argmax(), argmin(), and argsort() work similarly to the analogous numpy functions, except they operate on dictionaries and other Python builtin types.

The take() and compress() are generators, and also similar to their lesser known, but very useful numpy equivalents.

There are also other numpy inspired functions: unique(), argunique(), unique_flags(), and boolmask().

class ubelt.util_list.chunks(items, chunksize=None, nchunks=None, total=None, bordermode='none')

Bases: object

Generates successive n-sized chunks from items.

If the last chunk has less than n elements, bordermode is used to determine fill values.

Parameters:

• items (Iterable[T]) – input to iterate over
• chunksize (int) – size of each sublist yielded
• nchunks (int) – number of chunks to create ( cannot be specified if chunksize is specified)
• bordermode (str) – determines how to handle the last case if the length of the input is not divisible by chunksize valid values are: {‘none’, ‘cycle’, ‘replicate’}
• total (int) – hints about the length of the input

Yields:

List[T] – subsequent non-overlapping chunks of the input items

References

http://stackoverflow.com/questions/434287/iterate-over-a-list-in-chunks

Example

>>> import ubelt as ub
>>> items = '1234567'
>>> genresult = ub.chunks(items, chunksize=3)
>>> list(genresult)
[['1', '2', '3'], ['4', '5', '6'], ['7']]

Example

>>> import ubelt as ub
>>> items = [1, 2, 3, 4, 5, 6, 7]
>>> genresult = ub.chunks(items, chunksize=3, bordermode='none')
>>> assert list(genresult) == [[1, 2, 3], [4, 5, 6], [7]]
>>> genresult = ub.chunks(items, chunksize=3, bordermode='cycle')
>>> assert list(genresult) == [[1, 2, 3], [4, 5, 6], [7, 1, 2]]
>>> genresult = ub.chunks(items, chunksize=3, bordermode='replicate')
>>> assert list(genresult) == [[1, 2, 3], [4, 5, 6], [7, 7, 7]]

Example

>>> import ubelt as ub
>>> assert len(list(ub.chunks(range(2), nchunks=2))) == 2
>>> assert len(list(ub.chunks(range(3), nchunks=2))) == 2
>>> # Note: ub.chunks will not do the 2,1,1 split
>>> assert len(list(ub.chunks(range(4), nchunks=3))) == 2
>>> assert len(list(ub.chunks([], 2, None, 'none'))) == 0
>>> assert len(list(ub.chunks([], 2, None, 'cycle'))) == 0
>>> assert len(list(ub.chunks([], 2, None, 'replicate'))) == 0

Example

>>> def _check_len(self):
...     assert len(self) == len(list(self))
>>> _check_len(chunks(list(range(3)), nchunks=2))
>>> _check_len(chunks(list(range(2)), nchunks=2))
>>> _check_len(chunks(list(range(2)), nchunks=3))

Example

>>> import pytest
>>> assert pytest.raises(ValueError, chunks, range(9))
>>> assert pytest.raises(ValueError, chunks, range(9), chunksize=2, nchunks=2)
>>> assert pytest.raises(TypeError, len, chunks((_ for _ in range(2)), 2))

static noborder(items, chunksize)

static cycle(items, chunksize)

static replicate(items, chunksize)

ubelt.util_list.iterable(obj, strok=False)

Checks if the input implements the iterator interface. An exception is made for strings, which return False unless strok is True

Parameters:

• obj (object) – a scalar or iterable input
• strok (bool, default=False) – if True allow strings to be interpreted as iterable

Returns:

True if the input is iterable

Return type:

bool

Example

>>> obj_list = [3, [3], '3', (3,), [3, 4, 5], {}]
>>> result = [iterable(obj) for obj in obj_list]
>>> assert result == [False, True, False, True, True, True]
>>> result = [iterable(obj, strok=True) for obj in obj_list]
>>> assert result == [False, True, True, True, True, True]

ubelt.util_list.take(items, indices, default=NoParam)

Selects a subset of a list based on a list of indices.

This is similar to np.take, but pure python. This also supports specifying a default element if items is an iterable of dictionaries.

Parameters:

• items (Sequence[V] | Mapping[K, V]) – An indexable object to select items from
• indices (Iterable[int | K]) – sequence of indexes into items
• default (Any, default=NoParam) – if specified items must support the get method.

Yeilds:

V: a selected item within the list

SeeAlso:

ub.dict_subset()

Notes

ub.take(items, indices) is equivalent to (items[i] for i in indices) when default is unspecified.

Example

>>> import ubelt as ub
>>> items = [0, 1, 2, 3]
>>> indices = [2, 0]
>>> list(ub.take(items, indices))
[2, 0]

Example

>>> import ubelt as ub
>>> dict_ = {1: 'a', 2: 'b', 3: 'c'}
>>> keys = [1, 2, 3, 4, 5]
>>> result = list(ub.take(dict_, keys, None))
>>> assert result == ['a', 'b', 'c', None, None]

Example

>>> import ubelt as ub
>>> dict_ = {1: 'a', 2: 'b', 3: 'c'}
>>> keys = [1, 2, 3, 4, 5]
>>> try:
>>>     print(list(ub.take(dict_, keys)))
>>>     raise AssertionError('did not get key error')
>>> except KeyError:
>>>     print('correctly got key error')

ubelt.util_list.compress(items, flags)

Selects from items where the corresponding value in flags is True. This is similar to numpy.compress().

This is actually a simple alias for itertools.compress().

Parameters:

• items (Iterable[Any]) – a sequence to select items from
• flags (Iterable[bool]) – corresponding sequence of bools

Returns:

a subset of masked items

Return type:

Iterable[Any]

Example

>>> import ubelt as ub
>>> items = [1, 2, 3, 4, 5]
>>> flags = [False, True, True, False, True]
>>> list(ub.compress(items, flags))
[2, 3, 5]

ubelt.util_list.flatten(nested)

Transforms a nested iterable into a flat iterable.

This is simply an alias for itertools.chain.from_iterable().

Parameters:nested (Iterable[Iterable[Any]]) – list of lists Returns:flattened items Return type:Iterable[Any]

Example

>>> import ubelt as ub
>>> nested = [['a', 'b'], ['c', 'd']]
>>> list(ub.flatten(nested))
['a', 'b', 'c', 'd']

ubelt.util_list.unique(items, key=None)

Generates unique items in the order they appear.

Parameters:

• items (Iterable[A]) – list of items
• key (Callable[[A], B], default=None) – custom normalization function. If specified returns items where key(item) is unique.

Yields:

A – a unique item from the input sequence

Example

>>> import ubelt as ub
>>> items = [4, 6, 6, 0, 6, 1, 0, 2, 2, 1]
>>> unique_items = list(ub.unique(items))
>>> assert unique_items == [4, 6, 0, 1, 2]

Example

>>> import ubelt as ub
>>> items = ['A', 'a', 'b', 'B', 'C', 'c', 'D', 'e', 'D', 'E']
>>> unique_items = list(ub.unique(items, key=six.text_type.lower))
>>> assert unique_items == ['A', 'b', 'C', 'D', 'e']
>>> unique_items = list(ub.unique(items))
>>> assert unique_items == ['A', 'a', 'b', 'B', 'C', 'c', 'D', 'e', 'E']

ubelt.util_list.argunique(items, key=None)

Returns indices corresponding to the first instance of each unique item.

Parameters:

• items (Sequence[V]) – indexable collection of items
• key (Callable[[V], Any], default=None) – custom normalization function. If specified returns items where key(item) is unique.

Returns:

indices of the unique items

Return type:

Iterator[int]

Example

>>> items = [0, 2, 5, 1, 1, 0, 2, 4]
>>> indices = list(argunique(items))
>>> assert indices == [0, 1, 2, 3, 7]
>>> indices = list(argunique(items, key=lambda x: x % 2 == 0))
>>> assert indices == [0, 2]

ubelt.util_list.unique_flags(items, key=None)

Returns a list of booleans corresponding to the first instance of each unique item.

Parameters:

• items (Sequence) – indexable collection of items
• key (Callable[[V], Any], default=None) – custom normalization function. If specified returns items where key(item) is unique.

Returns:

flags the items that are unique

Return type:

List[bool]

Example

>>> import ubelt as ub
>>> items = [0, 2, 1, 1, 0, 9, 2]
>>> flags = unique_flags(items)
>>> assert flags == [True, True, True, False, False, True, False]
>>> flags = unique_flags(items, key=lambda x: x % 2 == 0)
>>> assert flags == [True, False, True, False, False, False, False]

ubelt.util_list.boolmask(indices, maxval=None)

Constructs a list of booleans where an item is True if its position is in indices otherwise it is False.

Parameters:

• indices (List[int]) – list of integer indices
• maxval (int) – length of the returned list. If not specified this is inferred using max(indices)

Returns:

mask - a list of booleans. mask[idx] is True if idx in indices

Return type:

List[bool]

Note

In the future the arg maxval may change its name to shape

Example

>>> import ubelt as ub
>>> indices = [0, 1, 4]
>>> mask = ub.boolmask(indices, maxval=6)
>>> assert mask == [True, True, False, False, True, False]
>>> mask = ub.boolmask(indices)
>>> assert mask == [True, True, False, False, True]

ubelt.util_list.iter_window(iterable, size=2, step=1, wrap=False)

Iterates through iterable with a window size. This is essentially a 1D sliding window.

Parameters:

• iterable (Iterable[T]) – an iterable sequence
• size (int, default=2) – sliding window size
• step (int, default=1) – sliding step size
• wrap (bool, default=False) – wraparound flag

Returns:

returns a possibly overlaping windows in a sequence

Return type:

Iterable[T]

Example

>>> iterable = [1, 2, 3, 4, 5, 6]
>>> size, step, wrap = 3, 1, True
>>> window_iter = iter_window(iterable, size, step, wrap)
>>> window_list = list(window_iter)
>>> print('window_list = %r' % (window_list,))
window_list = [(1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6), (5, 6, 1), (6, 1, 2)]

Example

>>> iterable = [1, 2, 3, 4, 5, 6]
>>> size, step, wrap = 3, 2, True
>>> window_iter = iter_window(iterable, size, step, wrap)
>>> window_list = list(window_iter)
>>> print('window_list = {!r}'.format(window_list))
window_list = [(1, 2, 3), (3, 4, 5), (5, 6, 1)]

Example

>>> iterable = [1, 2, 3, 4, 5, 6]
>>> size, step, wrap = 3, 2, False
>>> window_iter = iter_window(iterable, size, step, wrap)
>>> window_list = list(window_iter)
>>> print('window_list = {!r}'.format(window_list))
window_list = [(1, 2, 3), (3, 4, 5)]

Example

>>> iterable = []
>>> size, step, wrap = 3, 2, False
>>> window_iter = iter_window(iterable, size, step, wrap)
>>> window_list = list(window_iter)
>>> print('window_list = {!r}'.format(window_list))
window_list = []

ubelt.util_list.allsame(iterable, eq=<built-in function eq>)

Determine if all items in a sequence are the same

Parameters:

• iterable (Iterable[A]) – items to determine if they are all the same
• eq (Callable[[A, A], bool], default=operator.eq) – function used to test for equality

Returns:

True if all items are equal, otherwise False

Return type:

bool

Example

>>> allsame([1, 1, 1, 1])
True
>>> allsame([])
True
>>> allsame([0, 1])
False
>>> iterable = iter([0, 1, 1, 1])
>>> next(iterable)
>>> allsame(iterable)
True
>>> allsame(range(10))
False
>>> allsame(range(10), lambda a, b: True)
True

ubelt.util_list.argsort(indexable, key=None, reverse=False)

Returns the indices that would sort a indexable object.

This is similar to numpy.argsort(), but it is written in pure python and works on both lists and dictionaries.

Parameters:

• indexable (Iterable[B] | Mapping[A, B]) – indexable to sort by
• key (Callable[[A], B], default=None) – customizes the ordering of the indexable
• reverse (bool, default=False) – if True returns in descending order

Returns:

indices - list of indices such that sorts the indexable

Return type:

List[int]

Example

>>> import ubelt as ub
>>> # argsort works on dicts by returning keys
>>> dict_ = {'a': 3, 'b': 2, 'c': 100}
>>> indices = ub.argsort(dict_)
>>> assert list(ub.take(dict_, indices)) == sorted(dict_.values())
>>> # argsort works on lists by returning indices
>>> indexable = [100, 2, 432, 10]
>>> indices = ub.argsort(indexable)
>>> assert list(ub.take(indexable, indices)) == sorted(indexable)
>>> # Can use iterators, but be careful. It exhausts them.
>>> indexable = reversed(range(100))
>>> indices = ub.argsort(indexable)
>>> assert indices[0] == 99
>>> # Can use key just like sorted
>>> indexable = [[0, 1, 2], [3, 4], [5]]
>>> indices = ub.argsort(indexable, key=len)
>>> assert indices == [2, 1, 0]
>>> # Can use reverse just like sorted
>>> indexable = [0, 2, 1]
>>> indices = ub.argsort(indexable, reverse=True)
>>> assert indices == [1, 2, 0]

ubelt.util_list.argmax(indexable, key=None)

Returns index / key of the item with the largest value.

This is similar to numpy.argmax(), but it is written in pure python and works on both lists and dictionaries.

Parameters:

• indexable (Iterable[B] | Mapping[A, B]) – indexable to sort by
• key (Callable[[A], B], default=None) – customizes the ordering of the indexable

Returns:

the index of the item with the maximum value.

Return type:

int

Example

>>> assert argmax({'a': 3, 'b': 2, 'c': 100}) == 'c'
>>> assert argmax(['a', 'c', 'b', 'z', 'f']) == 3
>>> assert argmax([[0, 1], [2, 3, 4], [5]], key=len) == 1
>>> assert argmax({'a': 3, 'b': 2, 3: 100, 4: 4}) == 3
>>> assert argmax(iter(['a', 'c', 'b', 'z', 'f'])) == 3

ubelt.util_list.argmin(indexable, key=None)

Returns index / key of the item with the smallest value.

This is similar to numpy.argmin(), but it is written in pure python and works on both lists and dictionaries.

Parameters:

• indexable (Iterable[B] | Mapping[A, B]) – indexable to sort by
• key (Callable[[A], B], default=None) – customizes the ordering of the indexable

Returns:

the index of the item with the minimum value.

Return type:

int

Example

>>> assert argmin({'a': 3, 'b': 2, 'c': 100}) == 'b'
>>> assert argmin(['a', 'c', 'b', 'z', 'f']) == 0
>>> assert argmin([[0, 1], [2, 3, 4], [5]], key=len) == 2
>>> assert argmin({'a': 3, 'b': 2, 3: 100, 4: 4}) == 'b'
>>> assert argmin(iter(['a', 'c', 'A', 'z', 'f'])) == 2

ubelt.util_list.peek(iterable)

Look at the first item of an iterable. If the input is an iterator, then the next element is exhausted (i.e. a pop operation).

Parameters:iterable (List[T]) – an iterable Returns:

item - the first item of ordered sequence, a popped item from an

iterator, or an arbitrary item from an unordered collection.

Return type:T

Example

>>> import ubelt as ub
>>> data = [0, 1, 2]
>>> ub.peek(data)
0
>>> iterator = iter(data)
>>> print(ub.peek(iterator))
0
>>> print(ub.peek(iterator))
1
>>> print(ub.peek(iterator))
2
>>> ub.peek(range(3))
0