Source code for toolz.itertoolz

import itertools
import heapq
import collections
import operator
from functools import partial
from random import Random
from toolz.compatibility import (map, filterfalse, zip, zip_longest, iteritems,
                                 filter)
from toolz.utils import no_default


__all__ = ('remove', 'accumulate', 'groupby', 'merge_sorted', 'interleave',
           'unique', 'isiterable', 'isdistinct', 'take', 'drop', 'take_nth',
           'first', 'second', 'nth', 'last', 'get', 'concat', 'concatv',
           'mapcat', 'cons', 'interpose', 'frequencies', 'reduceby', 'iterate',
           'sliding_window', 'partition', 'partition_all', 'count', 'pluck',
           'join', 'tail', 'diff', 'topk', 'peek', 'random_sample')


[docs]def remove(predicate, seq): """ Return those items of sequence for which predicate(item) is False >>> def iseven(x): ... return x % 2 == 0 >>> list(remove(iseven, [1, 2, 3, 4])) [1, 3] """ return filterfalse(predicate, seq)
[docs]def accumulate(binop, seq, initial=no_default): """ Repeatedly apply binary function to a sequence, accumulating results >>> from operator import add, mul >>> list(accumulate(add, [1, 2, 3, 4, 5])) [1, 3, 6, 10, 15] >>> list(accumulate(mul, [1, 2, 3, 4, 5])) [1, 2, 6, 24, 120] Accumulate is similar to ``reduce`` and is good for making functions like cumulative sum: >>> from functools import partial, reduce >>> sum = partial(reduce, add) >>> cumsum = partial(accumulate, add) Accumulate also takes an optional argument that will be used as the first value. This is similar to reduce. >>> list(accumulate(add, [1, 2, 3], -1)) [-1, 0, 2, 5] >>> list(accumulate(add, [], 1)) [1] See Also: itertools.accumulate : In standard itertools for Python 3.2+ """ seq = iter(seq) result = next(seq) if initial == no_default else initial yield result for elem in seq: result = binop(result, elem) yield result
[docs]def groupby(key, seq): """ Group a collection by a key function >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] >>> groupby(len, names) # doctest: +SKIP {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} >>> iseven = lambda x: x % 2 == 0 >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} Non-callable keys imply grouping on a member. >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, ... {'name': 'Bob', 'gender': 'M'}, ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP {'F': [{'gender': 'F', 'name': 'Alice'}], 'M': [{'gender': 'M', 'name': 'Bob'}, {'gender': 'M', 'name': 'Charlie'}]} See Also: countby """ if not callable(key): key = getter(key) d = collections.defaultdict(lambda: [].append) for item in seq: d[key(item)](item) rv = {} for k, v in iteritems(d): rv[k] = v.__self__ return rv
[docs]def merge_sorted(*seqs, **kwargs): """ Merge and sort a collection of sorted collections This works lazily and only keeps one value from each iterable in memory. >>> list(merge_sorted([1, 3, 5], [2, 4, 6])) [1, 2, 3, 4, 5, 6] >>> ''.join(merge_sorted('abc', 'abc', 'abc')) 'aaabbbccc' The "key" function used to sort the input may be passed as a keyword. >>> list(merge_sorted([2, 3], [1, 3], key=lambda x: x // 3)) [2, 1, 3, 3] """ if len(seqs) == 0: return iter([]) elif len(seqs) == 1: return iter(seqs[0]) key = kwargs.get('key', None) if key is None: return _merge_sorted_binary(seqs) else: return _merge_sorted_binary_key(seqs, key)
def _merge_sorted_binary(seqs): mid = len(seqs) // 2 L1 = seqs[:mid] if len(L1) == 1: seq1 = iter(L1[0]) else: seq1 = _merge_sorted_binary(L1) L2 = seqs[mid:] if len(L2) == 1: seq2 = iter(L2[0]) else: seq2 = _merge_sorted_binary(L2) try: val2 = next(seq2) except StopIteration: for val1 in seq1: yield val1 return for val1 in seq1: if val2 < val1: yield val2 for val2 in seq2: if val2 < val1: yield val2 else: yield val1 break else: break else: yield val1 else: yield val2 for val2 in seq2: yield val2 return yield val1 for val1 in seq1: yield val1 def _merge_sorted_binary_key(seqs, key): mid = len(seqs) // 2 L1 = seqs[:mid] if len(L1) == 1: seq1 = iter(L1[0]) else: seq1 = _merge_sorted_binary_key(L1, key) L2 = seqs[mid:] if len(L2) == 1: seq2 = iter(L2[0]) else: seq2 = _merge_sorted_binary_key(L2, key) try: val2 = next(seq2) except StopIteration: for val1 in seq1: yield val1 return key2 = key(val2) for val1 in seq1: key1 = key(val1) if key2 < key1: yield val2 for val2 in seq2: key2 = key(val2) if key2 < key1: yield val2 else: yield val1 break else: break else: yield val1 else: yield val2 for val2 in seq2: yield val2 return yield val1 for val1 in seq1: yield val1
[docs]def interleave(seqs): """ Interleave a sequence of sequences >>> list(interleave([[1, 2], [3, 4]])) [1, 3, 2, 4] >>> ''.join(interleave(('ABC', 'XY'))) 'AXBYC' Both the individual sequences and the sequence of sequences may be infinite Returns a lazy iterator """ iters = itertools.cycle(map(iter, seqs)) while True: try: for itr in iters: yield next(itr) return except StopIteration: predicate = partial(operator.is_not, itr) iters = itertools.cycle(itertools.takewhile(predicate, iters))
[docs]def unique(seq, key=None): """ Return only unique elements of a sequence >>> tuple(unique((1, 2, 3))) (1, 2, 3) >>> tuple(unique((1, 2, 1, 3))) (1, 2, 3) Uniqueness can be defined by key keyword >>> tuple(unique(['cat', 'mouse', 'dog', 'hen'], key=len)) ('cat', 'mouse') """ seen = set() seen_add = seen.add if key is None: for item in seq: if item not in seen: seen_add(item) yield item else: # calculate key for item in seq: val = key(item) if val not in seen: seen_add(val) yield item
[docs]def isiterable(x): """ Is x iterable? >>> isiterable([1, 2, 3]) True >>> isiterable('abc') True >>> isiterable(5) False """ try: iter(x) return True except TypeError: return False
[docs]def isdistinct(seq): """ All values in sequence are distinct >>> isdistinct([1, 2, 3]) True >>> isdistinct([1, 2, 1]) False >>> isdistinct("Hello") False >>> isdistinct("World") True """ if iter(seq) is seq: seen = set() seen_add = seen.add for item in seq: if item in seen: return False seen_add(item) return True else: return len(seq) == len(set(seq))
[docs]def take(n, seq): """ The first n elements of a sequence >>> list(take(2, [10, 20, 30, 40, 50])) [10, 20] See Also: drop tail """ return itertools.islice(seq, n)
[docs]def tail(n, seq): """ The last n elements of a sequence >>> tail(2, [10, 20, 30, 40, 50]) [40, 50] See Also: drop take """ try: return seq[-n:] except (TypeError, KeyError): return tuple(collections.deque(seq, n))
[docs]def drop(n, seq): """ The sequence following the first n elements >>> list(drop(2, [10, 20, 30, 40, 50])) [30, 40, 50] See Also: take tail """ return itertools.islice(seq, n, None)
[docs]def take_nth(n, seq): """ Every nth item in seq >>> list(take_nth(2, [10, 20, 30, 40, 50])) [10, 30, 50] """ return itertools.islice(seq, 0, None, n)
[docs]def first(seq): """ The first element in a sequence >>> first('ABC') 'A' """ return next(iter(seq))
[docs]def second(seq): """ The second element in a sequence >>> second('ABC') 'B' """ return next(itertools.islice(seq, 1, None))
[docs]def nth(n, seq): """ The nth element in a sequence >>> nth(1, 'ABC') 'B' """ if isinstance(seq, (tuple, list, collections.Sequence)): return seq[n] else: return next(itertools.islice(seq, n, None))
[docs]def last(seq): """ The last element in a sequence >>> last('ABC') 'C' """ return tail(1, seq)[0]
rest = partial(drop, 1) def _get(ind, seq, default): try: return seq[ind] except (KeyError, IndexError): return default
[docs]def get(ind, seq, default=no_default): """ Get element in a sequence or dict Provides standard indexing >>> get(1, 'ABC') # Same as 'ABC'[1] 'B' Pass a list to get multiple values >>> get([1, 2], 'ABC') # ('ABC'[1], 'ABC'[2]) ('B', 'C') Works on any value that supports indexing/getitem For example here we see that it works with dictionaries >>> phonebook = {'Alice': '555-1234', ... 'Bob': '555-5678', ... 'Charlie':'555-9999'} >>> get('Alice', phonebook) '555-1234' >>> get(['Alice', 'Bob'], phonebook) ('555-1234', '555-5678') Provide a default for missing values >>> get(['Alice', 'Dennis'], phonebook, None) ('555-1234', None) See Also: pluck """ try: return seq[ind] except TypeError: # `ind` may be a list if isinstance(ind, list): if default == no_default: if len(ind) > 1: return operator.itemgetter(*ind)(seq) elif ind: return (seq[ind[0]],) else: return () else: return tuple(_get(i, seq, default) for i in ind) elif default != no_default: return default else: raise except (KeyError, IndexError): # we know `ind` is not a list if default == no_default: raise else: return default
[docs]def concat(seqs): """ Concatenate zero or more iterables, any of which may be infinite. An infinite sequence will prevent the rest of the arguments from being included. We use chain.from_iterable rather than ``chain(*seqs)`` so that seqs can be a generator. >>> list(concat([[], [1], [2, 3]])) [1, 2, 3] See also: itertools.chain.from_iterable equivalent """ return itertools.chain.from_iterable(seqs)
[docs]def concatv(*seqs): """ Variadic version of concat >>> list(concatv([], ["a"], ["b", "c"])) ['a', 'b', 'c'] See also: itertools.chain """ return concat(seqs)
[docs]def mapcat(func, seqs): """ Apply func to each sequence in seqs, concatenating results. >>> list(mapcat(lambda s: [c.upper() for c in s], ... [["a", "b"], ["c", "d", "e"]])) ['A', 'B', 'C', 'D', 'E'] """ return concat(map(func, seqs))
[docs]def cons(el, seq): """ Add el to beginning of (possibly infinite) sequence seq. >>> list(cons(1, [2, 3])) [1, 2, 3] """ return itertools.chain([el], seq)
[docs]def interpose(el, seq): """ Introduce element between each pair of elements in seq >>> list(interpose("a", [1, 2, 3])) [1, 'a', 2, 'a', 3] """ combined = zip(itertools.repeat(el), seq) return drop(1, concat(combined))
[docs]def frequencies(seq): """ Find number of occurrences of each value in seq >>> frequencies(['cat', 'cat', 'ox', 'pig', 'pig', 'cat']) #doctest: +SKIP {'cat': 3, 'ox': 1, 'pig': 2} See Also: countby groupby """ d = collections.defaultdict(int) for item in seq: d[item] += 1 return dict(d)
[docs]def reduceby(key, binop, seq, init=no_default): """ Perform a simultaneous groupby and reduction The computation: >>> result = reduceby(key, binop, seq, init) # doctest: +SKIP is equivalent to the following: >>> def reduction(group): # doctest: +SKIP ... return reduce(binop, group, init) # doctest: +SKIP >>> groups = groupby(key, seq) # doctest: +SKIP >>> result = valmap(reduction, groups) # doctest: +SKIP But the former does not build the intermediate groups, allowing it to operate in much less space. This makes it suitable for larger datasets that do not fit comfortably in memory The ``init`` keyword argument is the default initialization of the reduction. This can be either a constant value like ``0`` or a callable like ``lambda : 0`` as might be used in ``defaultdict``. Simple Examples --------------- >>> from operator import add, mul >>> iseven = lambda x: x % 2 == 0 >>> data = [1, 2, 3, 4, 5] >>> reduceby(iseven, add, data) # doctest: +SKIP {False: 9, True: 6} >>> reduceby(iseven, mul, data) # doctest: +SKIP {False: 15, True: 8} Complex Example --------------- >>> projects = [{'name': 'build roads', 'state': 'CA', 'cost': 1000000}, ... {'name': 'fight crime', 'state': 'IL', 'cost': 100000}, ... {'name': 'help farmers', 'state': 'IL', 'cost': 2000000}, ... {'name': 'help farmers', 'state': 'CA', 'cost': 200000}] >>> reduceby('state', # doctest: +SKIP ... lambda acc, x: acc + x['cost'], ... projects, 0) {'CA': 1200000, 'IL': 2100000} Example Using ``init`` ---------------------- >>> def set_add(s, i): ... s.add(i) ... return s >>> reduceby(iseven, set_add, [1, 2, 3, 4, 1, 2, 3], set) # doctest: +SKIP {True: set([2, 4]), False: set([1, 3])} """ is_no_default = init == no_default if not is_no_default and not callable(init): _init = init init = lambda: _init if not callable(key): key = getter(key) d = {} for item in seq: k = key(item) if k not in d: if is_no_default: d[k] = item continue else: d[k] = init() d[k] = binop(d[k], item) return d
[docs]def iterate(func, x): """ Repeatedly apply a function func onto an original input Yields x, then func(x), then func(func(x)), then func(func(func(x))), etc.. >>> def inc(x): return x + 1 >>> counter = iterate(inc, 0) >>> next(counter) 0 >>> next(counter) 1 >>> next(counter) 2 >>> double = lambda x: x * 2 >>> powers_of_two = iterate(double, 1) >>> next(powers_of_two) 1 >>> next(powers_of_two) 2 >>> next(powers_of_two) 4 >>> next(powers_of_two) 8 """ while True: yield x x = func(x)
[docs]def sliding_window(n, seq): """ A sequence of overlapping subsequences >>> list(sliding_window(2, [1, 2, 3, 4])) [(1, 2), (2, 3), (3, 4)] This function creates a sliding window suitable for transformations like sliding means / smoothing >>> mean = lambda seq: float(sum(seq)) / len(seq) >>> list(map(mean, sliding_window(2, [1, 2, 3, 4]))) [1.5, 2.5, 3.5] """ return zip(*(collections.deque(itertools.islice(it, i), 0) or it for i, it in enumerate(itertools.tee(seq, n))))
no_pad = '__no__pad__'
[docs]def partition(n, seq, pad=no_pad): """ Partition sequence into tuples of length n >>> list(partition(2, [1, 2, 3, 4])) [(1, 2), (3, 4)] If the length of ``seq`` is not evenly divisible by ``n``, the final tuple is dropped if ``pad`` is not specified, or filled to length ``n`` by pad: >>> list(partition(2, [1, 2, 3, 4, 5])) [(1, 2), (3, 4)] >>> list(partition(2, [1, 2, 3, 4, 5], pad=None)) [(1, 2), (3, 4), (5, None)] See Also: partition_all """ args = [iter(seq)] * n if pad is no_pad: return zip(*args) else: return zip_longest(*args, fillvalue=pad)
[docs]def partition_all(n, seq): """ Partition all elements of sequence into tuples of length at most n The final tuple may be shorter to accommodate extra elements. >>> list(partition_all(2, [1, 2, 3, 4])) [(1, 2), (3, 4)] >>> list(partition_all(2, [1, 2, 3, 4, 5])) [(1, 2), (3, 4), (5,)] See Also: partition """ args = [iter(seq)] * n it = zip_longest(*args, fillvalue=no_pad) try: prev = next(it) except StopIteration: return for item in it: yield prev prev = item if prev[-1] is no_pad: yield prev[:prev.index(no_pad)] else: yield prev
[docs]def count(seq): """ Count the number of items in seq Like the builtin ``len`` but works on lazy sequencies. Not to be confused with ``itertools.count`` See also: len """ if hasattr(seq, '__len__'): return len(seq) return sum(1 for i in seq)
[docs]def pluck(ind, seqs, default=no_default): """ plucks an element or several elements from each item in a sequence. ``pluck`` maps ``itertoolz.get`` over a sequence and returns one or more elements of each item in the sequence. This is equivalent to running `map(curried.get(ind), seqs)` ``ind`` can be either a single string/index or a list of strings/indices. ``seqs`` should be sequence containing sequences or dicts. e.g. >>> data = [{'id': 1, 'name': 'Cheese'}, {'id': 2, 'name': 'Pies'}] >>> list(pluck('name', data)) ['Cheese', 'Pies'] >>> list(pluck([0, 1], [[1, 2, 3], [4, 5, 7]])) [(1, 2), (4, 5)] See Also: get map """ if default == no_default: get = getter(ind) return map(get, seqs) elif isinstance(ind, list): return (tuple(_get(item, seq, default) for item in ind) for seq in seqs) return (_get(ind, seq, default) for seq in seqs)
def getter(index): if isinstance(index, list): if len(index) == 1: index = index[0] return lambda x: (x[index],) elif index: return operator.itemgetter(*index) else: return lambda x: () else: return operator.itemgetter(index)
[docs]def join(leftkey, leftseq, rightkey, rightseq, left_default=no_default, right_default=no_default): """ Join two sequences on common attributes This is a semi-streaming operation. The LEFT sequence is fully evaluated and placed into memory. The RIGHT sequence is evaluated lazily and so can be arbitrarily large. >>> friends = [('Alice', 'Edith'), ... ('Alice', 'Zhao'), ... ('Edith', 'Alice'), ... ('Zhao', 'Alice'), ... ('Zhao', 'Edith')] >>> cities = [('Alice', 'NYC'), ... ('Alice', 'Chicago'), ... ('Dan', 'Syndey'), ... ('Edith', 'Paris'), ... ('Edith', 'Berlin'), ... ('Zhao', 'Shanghai')] >>> # Vacation opportunities >>> # In what cities do people have friends? >>> result = join(second, friends, ... first, cities) >>> for ((a, b), (c, d)) in sorted(unique(result)): ... print((a, d)) ('Alice', 'Berlin') ('Alice', 'Paris') ('Alice', 'Shanghai') ('Edith', 'Chicago') ('Edith', 'NYC') ('Zhao', 'Chicago') ('Zhao', 'NYC') ('Zhao', 'Berlin') ('Zhao', 'Paris') Specify outer joins with keyword arguments ``left_default`` and/or ``right_default``. Here is a full outer join in which unmatched elements are paired with None. >>> identity = lambda x: x >>> list(join(identity, [1, 2, 3], ... identity, [2, 3, 4], ... left_default=None, right_default=None)) [(2, 2), (3, 3), (None, 4), (1, None)] Usually the key arguments are callables to be applied to the sequences. If the keys are not obviously callable then it is assumed that indexing was intended, e.g. the following is a legal change >>> # result = join(second, friends, first, cities) >>> result = join(1, friends, 0, cities) # doctest: +SKIP """ if not callable(leftkey): leftkey = getter(leftkey) if not callable(rightkey): rightkey = getter(rightkey) d = groupby(leftkey, leftseq) seen_keys = set() left_default_is_no_default = (left_default == no_default) for item in rightseq: key = rightkey(item) seen_keys.add(key) try: left_matches = d[key] for match in left_matches: yield (match, item) except KeyError: if not left_default_is_no_default: yield (left_default, item) if right_default != no_default: for key, matches in d.items(): if key not in seen_keys: for match in matches: yield (match, right_default)
[docs]def diff(*seqs, **kwargs): """ Return those items that differ between sequences >>> list(diff([1, 2, 3], [1, 2, 10, 100])) [(3, 10)] Shorter sequences may be padded with a ``default`` value: >>> list(diff([1, 2, 3], [1, 2, 10, 100], default=None)) [(3, 10), (None, 100)] A ``key`` function may also be applied to each item to use during comparisons: >>> list(diff(['apples', 'bananas'], ['Apples', 'Oranges'], key=str.lower)) [('bananas', 'Oranges')] """ N = len(seqs) if N == 1 and isinstance(seqs[0], list): seqs = seqs[0] N = len(seqs) if N < 2: raise TypeError('Too few sequences given (min 2 required)') default = kwargs.get('default', no_default) if default == no_default: iters = zip(*seqs) else: iters = zip_longest(*seqs, fillvalue=default) key = kwargs.get('key', None) if key is None: for items in iters: if items.count(items[0]) != N: yield items else: for items in iters: vals = tuple(map(key, items)) if vals.count(vals[0]) != N: yield items
[docs]def topk(k, seq, key=None): """ Find the k largest elements of a sequence Operates lazily in ``n*log(k)`` time >>> topk(2, [1, 100, 10, 1000]) (1000, 100) Use a key function to change sorted order >>> topk(2, ['Alice', 'Bob', 'Charlie', 'Dan'], key=len) ('Charlie', 'Alice') See also: heapq.nlargest """ if key is not None and not callable(key): key = getter(key) return tuple(heapq.nlargest(k, seq, key=key))
[docs]def peek(seq): """ Retrieve the next element of a sequence Returns the first element and an iterable equivalent to the original sequence, still having the element retrieved. >>> seq = [0, 1, 2, 3, 4] >>> first, seq = peek(seq) >>> first 0 >>> list(seq) [0, 1, 2, 3, 4] """ iterator = iter(seq) item = next(iterator) return item, itertools.chain([item], iterator)
[docs]def random_sample(prob, seq, random_state=None): """ Return elements from a sequence with probability of prob Returns a lazy iterator of random items from seq. ``random_sample`` considers each item independently and without replacement. See below how the first time it returned 13 items and the next time it returned 6 items. >>> seq = list(range(100)) >>> list(random_sample(0.1, seq)) # doctest: +SKIP [6, 9, 19, 35, 45, 50, 58, 62, 68, 72, 78, 86, 95] >>> list(random_sample(0.1, seq)) # doctest: +SKIP [6, 44, 54, 61, 69, 94] Providing an integer seed for ``random_state`` will result in deterministic sampling. Given the same seed it will return the same sample every time. >>> list(random_sample(0.1, seq, random_state=2016)) [7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98] >>> list(random_sample(0.1, seq, random_state=2016)) [7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98] ``random_state`` can also be any object with a method ``random`` that returns floats between 0.0 and 1.0 (exclusive). >>> from random import Random >>> randobj = Random(2016) >>> list(random_sample(0.1, seq, random_state=randobj)) [7, 9, 19, 25, 30, 32, 34, 48, 59, 60, 81, 98] """ if not hasattr(random_state, 'random'): random_state = Random(random_state) return filter(lambda _: random_state.random() < prob, seq)