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Doc/library/itertools.rst
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......@@ -2,14 +2,14 @@
=======================================================================
.. module:: itertools
:synopsis: Functions creating iterators for efficient looping.
:synopsis: Functions creating iterators for efficient looping.
.. moduleauthor:: Raymond Hettinger <python@rcn.com>
.. sectionauthor:: Raymond Hettinger <python@rcn.com>
.. testsetup::
from itertools import *
from itertools import *
This module implements a number of :term:`iterator` building blocks inspired
......@@ -46,7 +46,7 @@ Iterator Arguments Results
==================== ============================ ================================================= =============================================================
Iterator Arguments Results Example
==================== ============================ ================================================= =============================================================
:func:`accumulate` p[, start=0] p0, p0+p1, p0+p1+p2 ... ` ``accumulate([1,2,3,4,5]) --> 1 3 6 10 15``
:func:`accumulate` p[, start=0] p0, p0+p1, p0+p1+p2, ... ``accumulate([1,2,3,4,5]) --> 1 3 6 10 15``
:func:`chain` p, q, ... p0, p1, ... plast, q0, q1, ... ``chain('ABC', 'DEF') --> A B C D E F``
:func:`compress` data, selectors (d[0] if s[0]), (d[1] if s[1]), ... ``compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F``
:func:`dropwhile` pred, seq seq[n], seq[n+1], starting when pred fails ``dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1``
......@@ -102,44 +102,44 @@ loops that truncate the stream.
.. function:: chain(*iterables)
Make an iterator that returns elements from the first iterable until it is
exhausted, then proceeds to the next iterable, until all of the iterables are
exhausted. Used for treating consecutive sequences as a single sequence.
Equivalent to::
Make an iterator that returns elements from the first iterable until it is
exhausted, then proceeds to the next iterable, until all of the iterables are
exhausted. Used for treating consecutive sequences as a single sequence.
Equivalent to::
def chain(*iterables):
# chain('ABC', 'DEF') --> A B C D E F
for it in iterables:
for element in it:
yield element
def chain(*iterables):
# chain('ABC', 'DEF') --> A B C D E F
for it in iterables:
for element in it:
yield element
.. classmethod:: chain.from_iterable(iterable)
Alternate constructor for :func:`chain`. Gets chained inputs from a
single iterable argument that is evaluated lazily. Equivalent to::
Alternate constructor for :func:`chain`. Gets chained inputs from a
single iterable argument that is evaluated lazily. Equivalent to::
@classmethod
def from_iterable(iterables):
# chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
for it in iterables:
for element in it:
yield element
@classmethod
def from_iterable(iterables):
# chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
for it in iterables:
for element in it:
yield element
.. function:: combinations(iterable, r)
Return *r* length subsequences of elements from the input *iterable*.
Return *r* length subsequences of elements from the input *iterable*.
Combinations are emitted in lexicographic sort order. So, if the
input *iterable* is sorted, the combination tuples will be produced
in sorted order.
Combinations are emitted in lexicographic sort order. So, if the
input *iterable* is sorted, the combination tuples will be produced
in sorted order.
Elements are treated as unique based on their position, not on their
value. So if the input elements are unique, there will be no repeat
values in each combination.
Elements are treated as unique based on their position, not on their
value. So if the input elements are unique, there will be no repeat
values in each combination.
Equivalent to::
Equivalent to::
def combinations(iterable, r):
# combinations('ABCD', 2) --> AB AC AD BC BD CD
......@@ -161,9 +161,9 @@ loops that truncate the stream.
indices[j] = indices[j-1] + 1
yield tuple(pool[i] for i in indices)
The code for :func:`combinations` can be also expressed as a subsequence
of :func:`permutations` after filtering entries where the elements are not
in sorted order (according to their position in the input pool)::
The code for :func:`combinations` can be also expressed as a subsequence
of :func:`permutations` after filtering entries where the elements are not
in sorted order (according to their position in the input pool)::
def combinations(iterable, r):
pool = tuple(iterable)
......@@ -172,23 +172,23 @@ loops that truncate the stream.
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is ``n! / r! / (n-r)!`` when ``0 <= r <= n``
or zero when ``r > n``.
The number of items returned is ``n! / r! / (n-r)!`` when ``0 <= r <= n``
or zero when ``r > n``.
.. function:: combinations_with_replacement(iterable, r)
Return *r* length subsequences of elements from the input *iterable*
allowing individual elements to be repeated more than once.
Return *r* length subsequences of elements from the input *iterable*
allowing individual elements to be repeated more than once.
Combinations are emitted in lexicographic sort order. So, if the
input *iterable* is sorted, the combination tuples will be produced
in sorted order.
Combinations are emitted in lexicographic sort order. So, if the
input *iterable* is sorted, the combination tuples will be produced
in sorted order.
Elements are treated as unique based on their position, not on their
value. So if the input elements are unique, the generated combinations
will also be unique.
Elements are treated as unique based on their position, not on their
value. So if the input elements are unique, the generated combinations
will also be unique.
Equivalent to::
Equivalent to::
def combinations_with_replacement(iterable, r):
# combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
......@@ -207,9 +207,9 @@ loops that truncate the stream.
indices[i:] = [indices[i] + 1] * (r - i)
yield tuple(pool[i] for i in indices)
The code for :func:`combinations_with_replacement` can be also expressed as
a subsequence of :func:`product` after filtering entries where the elements
are not in sorted order (according to their position in the input pool)::
The code for :func:`combinations_with_replacement` can be also expressed as
a subsequence of :func:`product` after filtering entries where the elements
are not in sorted order (according to their position in the input pool)::
def combinations_with_replacement(iterable, r):
pool = tuple(iterable)
......@@ -218,196 +218,196 @@ loops that truncate the stream.
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is ``(n+r-1)! / r! / (n-1)!`` when ``n > 0``.
The number of items returned is ``(n+r-1)! / r! / (n-1)!`` when ``n > 0``.
.. versionadded:: 3.1
.. versionadded:: 3.1
.. function:: compress(data, selectors)
Make an iterator that filters elements from *data* returning only those that
have a corresponding element in *selectors* that evaluates to ``True``.
Stops when either the *data* or *selectors* iterables has been exhausted.
Equivalent to::
Make an iterator that filters elements from *data* returning only those that
have a corresponding element in *selectors* that evaluates to ``True``.
Stops when either the *data* or *selectors* iterables has been exhausted.
Equivalent to::
def compress(data, selectors):
# compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
return (d for d, s in zip(data, selectors) if s)
def compress(data, selectors):
# compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
return (d for d, s in zip(data, selectors) if s)
.. versionadded:: 3.1
.. versionadded:: 3.1
.. function:: count(start=0, step=1)
Make an iterator that returns evenly spaced values starting with *n*. Often
used as an argument to :func:`map` to generate consecutive data points.
Also, used with :func:`zip` to add sequence numbers. Equivalent to::
Make an iterator that returns evenly spaced values starting with *n*. Often
used as an argument to :func:`map` to generate consecutive data points.
Also, used with :func:`zip` to add sequence numbers. Equivalent to::
def count(start=0, step=1):
# count(10) --> 10 11 12 13 14 ...
# count(2.5, 0.5) -> 3.5 3.0 4.5 ...
n = start
while True:
yield n
n += step
def count(start=0, step=1):
# count(10) --> 10 11 12 13 14 ...
# count(2.5, 0.5) -> 3.5 3.0 4.5 ...
n = start
while True:
yield n
n += step
When counting with floating point numbers, better accuracy can sometimes be
achieved by substituting multiplicative code such as: ``(start + step * i
for i in count())``.
When counting with floating point numbers, better accuracy can sometimes be
achieved by substituting multiplicative code such as: ``(start + step * i
for i in count())``.
.. versionchanged:: 3.1
Added *step* argument and allowed non-integer arguments.
.. versionchanged:: 3.1
Added *step* argument and allowed non-integer arguments.
.. function:: cycle(iterable)
Make an iterator returning elements from the iterable and saving a copy of each.
When the iterable is exhausted, return elements from the saved copy. Repeats
indefinitely. Equivalent to::
def cycle(iterable):
# cycle('ABCD') --> A B C D A B C D A B C D ...
saved = []
for element in iterable:
yield element
saved.append(element)
while saved:
for element in saved:
Make an iterator returning elements from the iterable and saving a copy of each.
When the iterable is exhausted, return elements from the saved copy. Repeats
indefinitely. Equivalent to::
def cycle(iterable):
# cycle('ABCD') --> A B C D A B C D A B C D ...
saved = []
for element in iterable:
yield element
saved.append(element)
while saved:
for element in saved:
yield element
Note, this member of the toolkit may require significant auxiliary storage
(depending on the length of the iterable).
Note, this member of the toolkit may require significant auxiliary storage
(depending on the length of the iterable).
.. function:: dropwhile(predicate, iterable)
Make an iterator that drops elements from the iterable as long as the predicate
is true; afterwards, returns every element. Note, the iterator does not produce
*any* output until the predicate first becomes false, so it may have a lengthy
start-up time. Equivalent to::
def dropwhile(predicate, iterable):
# dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
iterable = iter(iterable)
for x in iterable:
if not predicate(x):
yield x
break
for x in iterable:
yield x
Make an iterator that drops elements from the iterable as long as the predicate
is true; afterwards, returns every element. Note, the iterator does not produce
*any* output until the predicate first becomes false, so it may have a lengthy
start-up time. Equivalent to::
def dropwhile(predicate, iterable):
# dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
iterable = iter(iterable)
for x in iterable:
if not predicate(x):
yield x
break
for x in iterable:
yield x
.. function:: filterfalse(predicate, iterable)
Make an iterator that filters elements from iterable returning only those for
which the predicate is ``False``. If *predicate* is ``None``, return the items
that are false. Equivalent to::
Make an iterator that filters elements from iterable returning only those for
which the predicate is ``False``. If *predicate* is ``None``, return the items
that are false. Equivalent to::
def filterfalse(predicate, iterable):
# filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
if predicate is None:
predicate = bool
for x in iterable:
if not predicate(x):
yield x
def filterfalse(predicate, iterable):
# filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
if predicate is None:
predicate = bool
for x in iterable:
if not predicate(x):
yield x
.. function:: groupby(iterable, key=None)
Make an iterator that returns consecutive keys and groups from the *iterable*.
The *key* is a function computing a key value for each element. If not
specified or is ``None``, *key* defaults to an identity function and returns
the element unchanged. Generally, the iterable needs to already be sorted on
the same key function.
The operation of :func:`groupby` is similar to the ``uniq`` filter in Unix. It
generates a break or new group every time the value of the key function changes
(which is why it is usually necessary to have sorted the data using the same key
function). That behavior differs from SQL's GROUP BY which aggregates common
elements regardless of their input order.
The returned group is itself an iterator that shares the underlying iterable
with :func:`groupby`. Because the source is shared, when the :func:`groupby`
object is advanced, the previous group is no longer visible. So, if that data
is needed later, it should be stored as a list::
groups = []
uniquekeys = []
data = sorted(data, key=keyfunc)
for k, g in groupby(data, keyfunc):
groups.append(list(g)) # Store group iterator as a list
uniquekeys.append(k)
:func:`groupby` is equivalent to::
class groupby:
# [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
# [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
def __init__(self, iterable, key=None):
if key is None:
key = lambda x: x
self.keyfunc = key
self.it = iter(iterable)
self.tgtkey = self.currkey = self.currvalue = object()
def __iter__(self):
return self
def __next__(self):
while self.currkey == self.tgtkey:
self.currvalue = next(self.it) # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
self.tgtkey = self.currkey
return (self.currkey, self._grouper(self.tgtkey))
def _grouper(self, tgtkey):
while self.currkey == tgtkey:
yield self.currvalue
self.currvalue = next(self.it) # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
Make an iterator that returns consecutive keys and groups from the *iterable*.
The *key* is a function computing a key value for each element. If not
specified or is ``None``, *key* defaults to an identity function and returns
the element unchanged. Generally, the iterable needs to already be sorted on
the same key function.
The operation of :func:`groupby` is similar to the ``uniq`` filter in Unix. It
generates a break or new group every time the value of the key function changes
(which is why it is usually necessary to have sorted the data using the same key
function). That behavior differs from SQL's GROUP BY which aggregates common
elements regardless of their input order.
The returned group is itself an iterator that shares the underlying iterable
with :func:`groupby`. Because the source is shared, when the :func:`groupby`
object is advanced, the previous group is no longer visible. So, if that data
is needed later, it should be stored as a list::
groups = []
uniquekeys = []
data = sorted(data, key=keyfunc)
for k, g in groupby(data, keyfunc):
groups.append(list(g)) # Store group iterator as a list
uniquekeys.append(k)
:func:`groupby` is equivalent to::
class groupby:
# [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
# [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
def __init__(self, iterable, key=None):
if key is None:
key = lambda x: x
self.keyfunc = key
self.it = iter(iterable)
self.tgtkey = self.currkey = self.currvalue = object()
def __iter__(self):
return self
def __next__(self):
while self.currkey == self.tgtkey:
self.currvalue = next(self.it) # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
self.tgtkey = self.currkey
return (self.currkey, self._grouper(self.tgtkey))
def _grouper(self, tgtkey):
while self.currkey == tgtkey:
yield self.currvalue
self.currvalue = next(self.it) # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
.. function:: islice(iterable, [start,] stop [, step])
Make an iterator that returns selected elements from the iterable. If *start* is
non-zero, then elements from the iterable are skipped until start is reached.
Afterward, elements are returned consecutively unless *step* is set higher than
one which results in items being skipped. If *stop* is ``None``, then iteration
continues until the iterator is exhausted, if at all; otherwise, it stops at the
specified position. Unlike regular slicing, :func:`islice` does not support
negative values for *start*, *stop*, or *step*. Can be used to extract related
fields from data where the internal structure has been flattened (for example, a
multi-line report may list a name field on every third line). Equivalent to::
def islice(iterable, *args):
# islice('ABCDEFG', 2) --> A B
# islice('ABCDEFG', 2, 4) --> C D
# islice('ABCDEFG', 2, None) --> C D E F G
# islice('ABCDEFG', 0, None, 2) --> A C E G
s = slice(*args)
it = iter(range(s.start or 0, s.stop or sys.maxsize, s.step or 1))
nexti = next(it)
for i, element in enumerate(iterable):
if i == nexti:
yield element
nexti = next(it)
If *start* is ``None``, then iteration starts at zero. If *step* is ``None``,
then the step defaults to one.
Make an iterator that returns selected elements from the iterable. If *start* is
non-zero, then elements from the iterable are skipped until start is reached.
Afterward, elements are returned consecutively unless *step* is set higher than
one which results in items being skipped. If *stop* is ``None``, then iteration
continues until the iterator is exhausted, if at all; otherwise, it stops at the
specified position. Unlike regular slicing, :func:`islice` does not support
negative values for *start*, *stop*, or *step*. Can be used to extract related
fields from data where the internal structure has been flattened (for example, a
multi-line report may list a name field on every third line). Equivalent to::
def islice(iterable, *args):
# islice('ABCDEFG', 2) --> A B
# islice('ABCDEFG', 2, 4) --> C D
# islice('ABCDEFG', 2, None) --> C D E F G
# islice('ABCDEFG', 0, None, 2) --> A C E G
s = slice(*args)
it = iter(range(s.start or 0, s.stop or sys.maxsize, s.step or 1))
nexti = next(it)
for i, element in enumerate(iterable):
if i == nexti:
yield element
nexti = next(it)
If *start* is ``None``, then iteration starts at zero. If *step* is ``None``,
then the step defaults to one.
.. function:: permutations(iterable, r=None)
Return successive *r* length permutations of elements in the *iterable*.
Return successive *r* length permutations of elements in the *iterable*.
If *r* is not specified or is ``None``, then *r* defaults to the length
of the *iterable* and all possible full-length permutations
are generated.
If *r* is not specified or is ``None``, then *r* defaults to the length
of the *iterable* and all possible full-length permutations
are generated.
Permutations are emitted in lexicographic sort order. So, if the
input *iterable* is sorted, the permutation tuples will be produced
in sorted order.
Permutations are emitted in lexicographic sort order. So, if the
input *iterable* is sorted, the permutation tuples will be produced
in sorted order.
Elements are treated as unique based on their position, not on their
value. So if the input elements are unique, there will be no repeat
values in each permutation.
Elements are treated as unique based on their position, not on their
value. So if the input elements are unique, there will be no repeat
values in each permutation.
Equivalent to::
Equivalent to::
def permutations(iterable, r=None):
# permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
......@@ -434,9 +434,9 @@ loops that truncate the stream.
else:
return
The code for :func:`permutations` can be also expressed as a subsequence of
:func:`product`, filtered to exclude entries with repeated elements (those
from the same position in the input pool)::
The code for :func:`permutations` can be also expressed as a subsequence of
:func:`product`, filtered to exclude entries with repeated elements (those
from the same position in the input pool)::
def permutations(iterable, r=None):
pool = tuple(iterable)
......@@ -446,87 +446,87 @@ loops that truncate the stream.
if len(set(indices)) == r:
yield tuple(pool[i] for i in indices)
The number of items returned is ``n! / (n-r)!`` when ``0 <= r <= n``
or zero when ``r > n``.
The number of items returned is ``n! / (n-r)!`` when ``0 <= r <= n``
or zero when ``r > n``.
.. function:: product(*iterables, repeat=1)
Cartesian product of input iterables.
Cartesian product of input iterables.
Equivalent to nested for-loops in a generator expression. For example,
``product(A, B)`` returns the same as ``((x,y) for x in A for y in B)``.
Equivalent to nested for-loops in a generator expression. For example,
``product(A, B)`` returns the same as ``((x,y) for x in A for y in B)``.
The nested loops cycle like an odometer with the rightmost element advancing
on every iteration. This pattern creates a lexicographic ordering so that if
the input's iterables are sorted, the product tuples are emitted in sorted
order.
The nested loops cycle like an odometer with the rightmost element advancing
on every iteration. This pattern creates a lexicographic ordering so that if
the input's iterables are sorted, the product tuples are emitted in sorted
order.
To compute the product of an iterable with itself, specify the number of
repetitions with the optional *repeat* keyword argument. For example,
``product(A, repeat=4)`` means the same as ``product(A, A, A, A)``.
To compute the product of an iterable with itself, specify the number of
repetitions with the optional *repeat* keyword argument. For example,
``product(A, repeat=4)`` means the same as ``product(A, A, A, A)``.
This function is equivalent to the following code, except that the
actual implementation does not build up intermediate results in memory::
This function is equivalent to the following code, except that the
actual implementation does not build up intermediate results in memory::
def product(*args, repeat=1):
# product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
# product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
pools = [tuple(pool) for pool in args] * repeat
result = [[]]
for pool in pools:
result = [x+[y] for x in result for y in pool]
for prod in result:
yield tuple(prod)
def product(*args, repeat=1):
# product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
# product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
pools = [tuple(pool) for pool in args] * repeat
result = [[]]
for pool in pools:
result = [x+[y] for x in result for y in pool]
for prod in result:
yield tuple(prod)
.. function:: repeat(object[, times])
Make an iterator that returns *object* over and over again. Runs indefinitely
unless the *times* argument is specified. Used as argument to :func:`map` for
invariant parameters to the called function. Also used with :func:`zip` to
create an invariant part of a tuple record. Equivalent to::
Make an iterator that returns *object* over and over again. Runs indefinitely
unless the *times* argument is specified. Used as argument to :func:`map` for
invariant parameters to the called function. Also used with :func:`zip` to
create an invariant part of a tuple record. Equivalent to::
def repeat(object, times=None):
# repeat(10, 3) --> 10 10 10
if times is None:
while True:
yield object
else:
for i in range(times):
yield object
def repeat(object, times=None):
# repeat(10, 3) --> 10 10 10
if times is None:
while True:
yield object
else:
for i in range(times):
yield object
.. function:: starmap(function, iterable)
Make an iterator that computes the function using arguments obtained from
the iterable. Used instead of :func:`map` when argument parameters are already
grouped in tuples from a single iterable (the data has been "pre-zipped"). The
difference between :func:`map` and :func:`starmap` parallels the distinction
between ``function(a,b)`` and ``function(*c)``. Equivalent to::
Make an iterator that computes the function using arguments obtained from
the iterable. Used instead of :func:`map` when argument parameters are already
grouped in tuples from a single iterable (the data has been "pre-zipped"). The
difference between :func:`map` and :func:`starmap` parallels the distinction
between ``function(a,b)`` and ``function(*c)``. Equivalent to::
def starmap(function, iterable):
# starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
for args in iterable:
yield function(*args)
def starmap(function, iterable):
# starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
for args in iterable:
yield function(*args)
.. function:: takewhile(predicate, iterable)
Make an iterator that returns elements from the iterable as long as the
predicate is true. Equivalent to::
Make an iterator that returns elements from the iterable as long as the
predicate is true. Equivalent to::
def takewhile(predicate, iterable):
# takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
for x in iterable:
if predicate(x):
yield x
else:
break
def takewhile(predicate, iterable):
# takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
for x in iterable:
if predicate(x):
yield x
else:
break
.. function:: tee(iterable, n=2)
Return *n* independent iterators from a single iterable. Equivalent to::
Return *n* independent iterators from a single iterable. Equivalent to::
def tee(iterable, n=2):
it = iter(iterable)
......@@ -540,38 +540,38 @@ loops that truncate the stream.
yield mydeque.popleft()
return tuple(gen(d) for d in deques)
Once :func:`tee` has made a split, the original *iterable* should not be
used anywhere else; otherwise, the *iterable* could get advanced without
the tee objects being informed.
Once :func:`tee` has made a split, the original *iterable* should not be
used anywhere else; otherwise, the *iterable* could get advanced without
the tee objects being informed.
This itertool may require significant auxiliary storage (depending on how
much temporary data needs to be stored). In general, if one iterator uses
most or all of the data before another iterator starts, it is faster to use
:func:`list` instead of :func:`tee`.
This itertool may require significant auxiliary storage (depending on how
much temporary data needs to be stored). In general, if one iterator uses
most or all of the data before another iterator starts, it is faster to use
:func:`list` instead of :func:`tee`.
.. function:: zip_longest(*iterables, fillvalue=None)
Make an iterator that aggregates elements from each of the iterables. If the
iterables are of uneven length, missing values are filled-in with *fillvalue*.
Iteration continues until the longest iterable is exhausted. Equivalent to::
Make an iterator that aggregates elements from each of the iterables. If the
iterables are of uneven length, missing values are filled-in with *fillvalue*.
Iteration continues until the longest iterable is exhausted. Equivalent to::
def zip_longest(*args, fillvalue=None):
# zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
def sentinel(counter = ([fillvalue]*(len(args)-1)).pop):
yield counter() # yields the fillvalue, or raises IndexError
fillers = repeat(fillvalue)
iters = [chain(it, sentinel(), fillers) for it in args]
try:
for tup in zip(*iters):
yield tup
except IndexError:
pass
def zip_longest(*args, fillvalue=None):
# zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
def sentinel(counter = ([fillvalue]*(len(args)-1)).pop):
yield counter() # yields the fillvalue, or raises IndexError
fillers = repeat(fillvalue)
iters = [chain(it, sentinel(), fillers) for it in args]
try:
for tup in zip(*iters):
yield tup
except IndexError:
pass
If one of the iterables is potentially infinite, then the :func:`zip_longest`
function should be wrapped with something that limits the number of calls
(for example :func:`islice` or :func:`takewhile`). If not specified,
*fillvalue* defaults to ``None``.
If one of the iterables is potentially infinite, then the :func:`zip_longest`
function should be wrapped with something that limits the number of calls
(for example :func:`islice` or :func:`takewhile`). If not specified,
*fillvalue* defaults to ``None``.
.. _itertools-recipes:
......@@ -592,168 +592,168 @@ which incur interpreter overhead.
.. testcode::
def take(n, iterable):
"Return first n items of the iterable as a list"
return list(islice(iterable, n))
def tabulate(function, start=0):
"Return function(0), function(1), ..."
return map(function, count(start))
def consume(iterator, n):
"Advance the iterator n-steps ahead. If n is none, consume entirely."
# Use functions that consume iterators at C speed.
if n is None:
# feed the entire iterator into a zero-length deque
collections.deque(iterator, maxlen=0)
else:
# advance to the empty slice starting at position n
next(islice(iterator, n, n), None)
def nth(iterable, n, default=None):
"Returns the nth item or a default value"
return next(islice(iterable, n, None), default)
def quantify(iterable, pred=bool):
"Count how many times the predicate is true"
return sum(map(pred, iterable))
def padnone(iterable):
"""Returns the sequence elements and then returns None indefinitely.
Useful for emulating the behavior of the built-in map() function.
"""
return chain(iterable, repeat(None))
def ncycles(iterable, n):
"Returns the sequence elements n times"
return chain.from_iterable(repeat(tuple(iterable), n))
def dotproduct(vec1, vec2):
return sum(map(operator.mul, vec1, vec2))
def flatten(listOfLists):
"Flatten one level of nesting"
return chain.from_iterable(listOfLists)
def repeatfunc(func, times=None, *args):
"""Repeat calls to func with specified arguments.
Example: repeatfunc(random.random)
"""
if times is None:
return starmap(func, repeat(args))
return starmap(func, repeat(args, times))
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return zip(a, b)
def grouper(n, iterable, fillvalue=None):
"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
def roundrobin(*iterables):
"roundrobin('ABC', 'D', 'EF') --> A D E B F C"
# Recipe credited to George Sakkis
pending = len(iterables)
nexts = cycle(iter(it).__next__ for it in iterables)
while pending:
try:
for next in nexts:
yield next()
except StopIteration:
pending -= 1
nexts = cycle(islice(nexts, pending))
def partition(pred, iterable):
'Use a predicate to partition entries into false entries and true entries'
# partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9
t1, t2 = tee(iterable)
return filterfalse(pred, t1), filter(pred, t2)
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
def unique_everseen(iterable, key=None):
"List unique elements, preserving order. Remember all elements ever seen."
# unique_everseen('AAAABBBCCDAABBB') --> A B C D
# unique_everseen('ABBCcAD', str.lower) --> A B C D
seen = set()
seen_add = seen.add
if key is None:
for element in filterfalse(seen.__contains__, iterable):
seen_add(element)
yield element
else:
for element in iterable:
k = key(element)
if k not in seen:
seen_add(k)
yield element
def unique_justseen(iterable, key=None):
"List unique elements, preserving order. Remember only the element just seen."
# unique_justseen('AAAABBBCCDAABBB') --> A B C D A B
# unique_justseen('ABBCcAD', str.lower) --> A B C A D
return map(next, map(itemgetter(1), groupby(iterable, key)))
def iter_except(func, exception, first=None):
""" Call a function repeatedly until an exception is raised.
Converts a call-until-exception interface to an iterator interface.
Like __builtin__.iter(func, sentinel) but uses an exception instead
of a sentinel to end the loop.
Examples:
iter_except(functools.partial(heappop, h), IndexError) # priority queue iterator
iter_except(d.popitem, KeyError) # non-blocking dict iterator
iter_except(d.popleft, IndexError) # non-blocking deque iterator
iter_except(q.get_nowait, Queue.Empty) # loop over a producer Queue
iter_except(s.pop, KeyError) # non-blocking set iterator
"""
try:
if first is not None:
yield first() # For database APIs needing an initial cast to db.first()
while 1:
yield func()
except exception:
pass
def random_product(*args, repeat=1):
"Random selection from itertools.product(*args, **kwds)"
pools = [tuple(pool) for pool in args] * repeat
return tuple(random.choice(pool) for pool in pools)
def random_permutation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterable)
r = len(pool) if r is None else r
return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.sample(range(n), r))
return tuple(pool[i] for i in indices)
def random_combination_with_replacement(iterable, r):
"Random selection from itertools.combinations_with_replacement(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.randrange(n) for i in range(r))
return tuple(pool[i] for i in indices)
def take(n, iterable):
"Return first n items of the iterable as a list"
return list(islice(iterable, n))
def tabulate(function, start=0):
"Return function(0), function(1), ..."
return map(function, count(start))
def consume(iterator, n):
"Advance the iterator n-steps ahead. If n is none, consume entirely."
# Use functions that consume iterators at C speed.
if n is None:
# feed the entire iterator into a zero-length deque
collections.deque(iterator, maxlen=0)
else:
# advance to the empty slice starting at position n
next(islice(iterator, n, n), None)
def nth(iterable, n, default=None):
"Returns the nth item or a default value"
return next(islice(iterable, n, None), default)
def quantify(iterable, pred=bool):
"Count how many times the predicate is true"
return sum(map(pred, iterable))
def padnone(iterable):
"""Returns the sequence elements and then returns None indefinitely.
Useful for emulating the behavior of the built-in map() function.
"""
return chain(iterable, repeat(None))
def ncycles(iterable, n):
"Returns the sequence elements n times"
return chain.from_iterable(repeat(tuple(iterable), n))
def dotproduct(vec1, vec2):
return sum(map(operator.mul, vec1, vec2))
def flatten(listOfLists):
"Flatten one level of nesting"
return chain.from_iterable(listOfLists)
def repeatfunc(func, times=None, *args):
"""Repeat calls to func with specified arguments.
Example: repeatfunc(random.random)
"""
if times is None:
return starmap(func, repeat(args))
return starmap(func, repeat(args, times))
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return zip(a, b)
def grouper(n, iterable, fillvalue=None):
"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
def roundrobin(*iterables):
"roundrobin('ABC', 'D', 'EF') --> A D E B F C"
# Recipe credited to George Sakkis
pending = len(iterables)
nexts = cycle(iter(it).__next__ for it in iterables)
while pending:
try:
for next in nexts:
yield next()
except StopIteration:
pending -= 1
nexts = cycle(islice(nexts, pending))
def partition(pred, iterable):
'Use a predicate to partition entries into false entries and true entries'
# partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9
t1, t2 = tee(iterable)
return filterfalse(pred, t1), filter(pred, t2)
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
def unique_everseen(iterable, key=None):
"List unique elements, preserving order. Remember all elements ever seen."
# unique_everseen('AAAABBBCCDAABBB') --> A B C D
# unique_everseen('ABBCcAD', str.lower) --> A B C D
seen = set()
seen_add = seen.add
if key is None:
for element in filterfalse(seen.__contains__, iterable):
seen_add(element)
yield element
else:
for element in iterable:
k = key(element)
if k not in seen:
seen_add(k)
yield element
def unique_justseen(iterable, key=None):
"List unique elements, preserving order. Remember only the element just seen."
# unique_justseen('AAAABBBCCDAABBB') --> A B C D A B
# unique_justseen('ABBCcAD', str.lower) --> A B C A D
return map(next, map(itemgetter(1), groupby(iterable, key)))
def iter_except(func, exception, first=None):
""" Call a function repeatedly until an exception is raised.
Converts a call-until-exception interface to an iterator interface.
Like __builtin__.iter(func, sentinel) but uses an exception instead
of a sentinel to end the loop.
Examples:
iter_except(functools.partial(heappop, h), IndexError) # priority queue iterator
iter_except(d.popitem, KeyError) # non-blocking dict iterator
iter_except(d.popleft, IndexError) # non-blocking deque iterator
iter_except(q.get_nowait, Queue.Empty) # loop over a producer Queue
iter_except(s.pop, KeyError) # non-blocking set iterator
"""
try:
if first is not None:
yield first() # For database APIs needing an initial cast to db.first()
while 1:
yield func()
except exception:
pass
def random_product(*args, repeat=1):
"Random selection from itertools.product(*args, **kwds)"
pools = [tuple(pool) for pool in args] * repeat
return tuple(random.choice(pool) for pool in pools)
def random_permutation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterable)
r = len(pool) if r is None else r
return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.sample(range(n), r))
return tuple(pool[i] for i in indices)
def random_combination_with_replacement(iterable, r):
"Random selection from itertools.combinations_with_replacement(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.randrange(n) for i in range(r))
return tuple(pool[i] for i in indices)
Note, many of the above recipes can be optimized by replacing global lookups
with local variables defined as default values. For example, the
*dotproduct* recipe can be written as::
def dotproduct(vec1, vec2, sum=sum, map=map, mul=operator.mul):
return sum(map(mul, vec1, vec2))
def dotproduct(vec1, vec2, sum=sum, map=map, mul=operator.mul):
return sum(map(mul, vec1, vec2))
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