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')
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)
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
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
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
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))
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
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
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))
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)
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))
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)
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)
def first(seq):
""" The first element in a sequence
>>> first('ABC')
'A'
"""
return next(iter(seq))
def second(seq):
""" The second element in a sequence
>>> second('ABC')
'B'
"""
return next(itertools.islice(seq, 1, None))
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))
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
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
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)
def concatv(*seqs):
""" Variadic version of concat
>>> list(concatv([], ["a"], ["b", "c"]))
['a', 'b', 'c']
See also:
itertools.chain
"""
return concat(seqs)
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))
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)
def interpose(el, seq):
""" Introduce element between each pair of elements in seq
>>> list(interpose("a", [1, 2, 3]))
[1, 'a', 2, 'a', 3]
"""
inposed = concat(zip(itertools.repeat(el), seq))
next(inposed)
return inposed
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)
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
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)
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__'
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)
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
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)
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)
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)
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
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))
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)
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)