Sequences¶
This functions are aimed at manipulating finite and infinite sequences of values. Some functions have two flavors: one returning list and other returning possibly infinite iterator, the latter ones follow convention of prepending i
before listreturning function name.
When working with sequences, see also itertools
standard module. Funcy reexports and aliases some functions from it.
Generate¶

repeat
(item[, n])¶ Makes an iterator yielding
item
forn
times or indefinitely ifn
is omitted.repeat()
simply repeat given value, when you need to reevaluate something repeatedly userepeatedly()
instead.When you just need a length
n
list or tuple ofitem
you can use:[item] * n # or (item,) * n

count
(start=0, step=1)¶ Makes infinite iterator of values:
start, start + step, start + 2*step, ...
.Could be used to generate sequence:
imap(lambda x: x ** 2, count(1)) # > 1, 4, 9, 16, ...
Or annotate sequence using
zip()
orizip()
:zip(count(), 'abcd') # > [(0, 'a'), (1, 'b'), (2, 'c'), (3, 'd')] # print code with BASICstyle numbered lines for line in izip(count(10, 10), code.splitlines()): print '%d %s' % line
See also
enumerate()
and originalitertools.count()
documentation.

cycle
(seq)¶ Cycles passed
seq
indefinitely returning its elements one by one.Useful when you need to cyclically decorate some sequence:
for n, parity in izip(count(), cycle(['even', 'odd'])): print '%d is %s' % (n, parity)

repeatedly
(f[, n])¶ Takes a function of no args, presumably with side effects, and returns an infinite (or length
n
if supplied) iterator of calls to it.For example, this call can be used to generate 10 random numbers:
repeatedly(random.random, 10)
Or one can create a length
n
list of freshlycreated objects of same type:repeatedly(list, n)

iterate
(f, x)¶ Returns an infinite iterator of
x, f(x), f(f(x)), ...
etc.Most common use is to generate some recursive sequence:
iterate(inc, 5) # > 5, 6, 7, 8, 9, ... iterate(lambda x: x * 2, 1) # > 1, 2, 4, 8, 16, ... step = lambda ((a, b)): (b, a + b) imap(first, iterate(step, (0, 1))) # > 0, 1, 1, 2, 3, 5, 8, ... (Fibonacci sequence)
Manipulate¶
This section provides some robust tools for sequence slicing. Consider Slicings or itertools.islice()
for more generic cases.

take
(n, seq)¶ Returns a list of the first
n
items in the sequence, or all items if there are fewer thann
.take(3, [2, 3, 4, 5]) # [2, 3, 4] take(3, count(5)) # [5, 6, 7] take(3, 'ab') # ['a', 'b']

drop
(n, seq)¶ Skips first
n
items in the sequence, returning iterator yielding rest of its items.drop(3, [2, 3, 4, 5]) # iter([5]) drop(3, count(5)) # count(8) drop(3, 'ab') # empty iterator

first
(seq)¶ Returns the first item in the sequence. Returns
None
if the sequence is empty. Typical usage is choosing first of some generated variants:# Get a text message of first failed validation rule fail = first(rule.text for rule in rules if not rule.test(instance)) # Use simple pattern matching to construct form field widget TYPE_TO_WIDGET = ( [lambda f: f.choices, lambda f: Select(choices=f.choices)], [lambda f: f.type == 'int', lambda f: TextInput(coerce=int)], [lambda f: f.type == 'string', lambda f: TextInput()], [lambda f: f.type == 'text', lambda f: Textarea()], [lambda f: f.type == 'boolean', lambda f: Checkbox(f.label)], ) return first(do(field) for cond, do in TYPE_TO_WIDGET if cond(field))
Other common use case is passing to
map()
orimap()
. See last example initerate()
for such example.

second
(seq)¶ Returns the second item in given sequence. Returns
None
if there are less than two items in it.Could come in handy with sequences of pairs, e.g.
dict.items()
. Following code extract values of a dict sorted by keys:map(second, sorted(some_dict.items()))
And this line constructs an ordered by value dict from a plain one:
OrderedDict(sorted(plain_dict.items(), key=second))

nth
(n, seq)¶ Returns nth item in sequence or
None
if no one exists. Items are counted from 0, so it’s like indexed access but works for iterators. E.g. here is how one can get 6th line of some_file:nth(5, repeatedly(open('some_file').readline))

last
(seq)¶ Returns the last item in the sequence. Returns
None
if the sequence is empty. Tries to be efficient when sequence supports indexed or reversed access and fallbacks to iterating over it if not.

rest
(seq)¶ Skips first item in the sequence, returning iterator starting just after it. A shortcut for
drop(1, seq)
.

butlast
(seq)¶ Returns an iterator of all elements of the sequence but last.

ilen
(seq)¶ Calculates length of iterator. Will consume it or hang up if it’s infinite.
Especially useful in conjunction with filtering or slicing functions, for example, this way one can find common start length of two strings:
ilen(takewhile(lambda (x, y): x == y, zip(s1, s2)))
Unite¶

concat
(*seqs)¶ 
iconcat
(*seqs)¶ Concats several sequences into one.
iconcat()
returns an iterator yielding concatenation.iconcat()
is an alias foritertools.chain()
.

cat
(seqs)¶ 
icat
(seqs)¶ Concatenates passed sequences. Useful when dealing with sequence of sequences, see
concat()
oriconcat()
to join just a few sequences.Flattening of various nested sequences is most common use:
# Flatten two level deep list cat(list_of_lists) # Get a flat html of errors of a form errors = icat(inline.errors() for inline in form) error_text = '<br>'.join(errors) # Brace expansion on product of sums # (a + b)(t + pq)x == atx + apqx + btx + bpqx terms = [['a', 'b'], ['t', 'pq'], ['x']] map(cat, product(*terms)) # [list('atx'), list('apqx'), list('btx'), list('bpqx')]
icat()
is an alias foritertools.chain.from_iterable()
.

flatten
(seq, follow=is_seqcont)¶ 
iflatten
(seq, follow=is_seqcont)¶ Flattens arbitrary nested sequence of values and other sequences.
follow
argument determines whether to unpack each item. By default it dives into lists, tuples and iterators, seeis_seqcont()
for further explanation.See also
cat()
oricat()
if you need to flatten strictly twolevel sequence of sequences.

tree_leaves
(root, follow=is_seqcont, children=iter)¶ 
itree_leaves
(root, follow=is_seqcont, children=iter)¶ A way to list or iterate over all the tree leaves. E.g. this is how you can list all descendants of a class:
tree_leaves(Base, children=type.__subclasses__, follow=type.__subclasses__)

tree_nodes
(root, follow=is_seqcont, children=iter)¶ 
itree_nodes
(root, follow=is_seqcont, children=iter)¶ A way to list or iterate over all the tree nodes. E.g. this is how you can list all classes in hierarchy:
tree_nodes(Base, children=type.__subclasses__, follow=type.__subclasses__)

interleave
(*seqs)¶ Returns an iterator yielding first item in each sequence, then second and so on until some sequence ends. Numbers of items taken from all sequences are always equal.

interpose
(sep, seq)¶ Returns an iterator yielding elements of
seq
separated bysep
.Helpful when
str.join()
is not good enough. This code is a part of translator working with operation node:def visit_BoolOp(self, node): # ... do generic visit node.code = mapcat(translate, interpose(node.op, node.values))
Transform and filter¶
Most of functions in this section support Extended function semantics. Among other things it allows to rewrite examples using re_tester()
and re_finder()
tighter.

remove
(pred, seq)¶ 
iremove
(pred, seq)¶ Return a list or an iterator of items of
seq
that result in false when passed topred
. The results of this functions complement results of standardfilter()
andifilter()
.A handy use is passing
re_tester()
result aspred
. For example, this code removes any whitespaceonly lines from list:remove(re_tester('^\s+$'), lines)
Note, you can rewrite it shorter using Extended function semantics:
remove('^\s+$', lines)

keep
([f, ]seq)¶ 
ikeep
([f, ]seq)¶ Maps
seq
with given function and then filters out falsy elements. Simply filtersseq
whenf
is absent. In fact these functions are just handy shortcuts:keep(f, seq) == filter(bool, map(f, seq)) keep(seq) == filter(bool, seq) ikeep(f, seq) == ifilter(bool, imap(f, seq)) ikeep(seq) == ifilter(bool, seq)
Natural use case for
keep()
is data extraction or recognition that could eventually fail:# Extract numbers from words keep(re_finder(r'\d+'), words) # Recognize as many colors by name as possible keep(COLOR_BY_NAME.get, color_names)
An iterator version can be useful when you don’t need or not sure you need the whole sequence. For example, you can use
first()
ikeep()
combo to find out first match:first(ikeep(COLOR_BY_NAME.get, color_name_candidates))
Alternatively, you can do the same with
some()
andimap()
.One argument variant is a simple tool to keep your data free of falsy junk. This one returns nonempty description lines:
keep(description.splitlines())
Other common case is using generator expression instead of mapping function. Consider these two lines:
keep(f.name for f in fields) # sugar generator expression keep(attrgetter('name'), fields) # pure functions

mapcat
(f, *seqs)¶ 
imapcat
(f, *seqs)¶ Maps given sequence(s) and then concatenates results, essentially a shortcut for
cat(map(f, *seqs))
. Come in handy when extracting multiple values from every sequence item or transforming nested sequences:# Get all the lines of all the texts in single flat list mapcat(str.splitlines, bunch_of_texts) # Extract all numbers from strings mapcat(partial(re_all, r'\d+'), bunch_of_strings)

without
(seq, *items)¶ 
iwithout
(seq, *items)¶ Returns sequence with
items
removed, preserves order. Designed to work with a fewitems
, this allows removing unhashable objects:no_empty_lists = without(lists, [])
In case of large amount of unwanted elements one can use
remove()
:remove(set(unwanted_elements), seq)
Or simple set difference if order of sequence is irrelevant.
Split and chunk¶

split
(pred, seq)¶ 
isplit
(pred, seq)¶ Splits sequence items which pass predicate from the ones that don’t, essentially returning a tuple
filter(pred, seq), remove(pred, seq)
.For example, this way one can separate private attributes of an instance from public ones:
private, public = split(re_tester('^_'), dir(instance))
Split absolute and relative urls using extended predicate semantics:
absolute, relative = split(r'^http://', urls)

split_at
(n, seq)¶ 
isplit_at
(n, seq)¶ Splits sequence at given position, returning a tuple of its start and tail.

split_by
(pred, seq)¶ 
isplit_by
(pred, seq)¶ Splits start of sequence, consisting of items passing predicate, from the rest of it. Works similar to
takewhile(pred, seq), dropwhile(pred, seq)
, but returns lists and works with iteratorseq
correctly:split_by(bool, iter([2, 1, 0, 1, 2])) # [2, 1], [0, 1, 2]

takewhile
([pred, ]seq)¶ Yeilds elements of
seq
as long as they passpred
. Stops on first one which makes predicate falsy:# Extract first paragraph of text takewhile(re_tester(r'\S'), text.splitlines()) # Build path from node to tree root takewhile(bool, iterate(attrgetter('parent'), node))

dropwhile
([pred, ]seq)¶ This is a mirror of
takewhile()
. Skips elements of given sequence whilepred
is true and yields the rest of it:# Skip leading whitespaceonly lines dropwhile(re_tester('^\s*$'), text_lines)

group_by
(f, seq)¶ Groups elements of
seq
keyed by the result off
. The value at each key will be a list of the corresponding elements, in the order they appear inseq
. Returnsdefaultdict(list)
.stats = group_by(len, ['a', 'ab', 'b']) stats[1] # > ['a', 'b'] stats[2] # > ['ab'] stats[3] # > [], since stats is defaultdict
One can use
split()
when grouping by boolean predicate. See alsoitertools.groupby()
.

group_by_keys
(get_keys, seq)¶ Groups elements of
seq
having multiple keys each intodefaultdict(list)
. Can be used to reverse grouping:posts_by_tag = group_by_keys(attrgetter(tags), posts) sentences_with_word = group_by_keys(str.split, sentences)

group_values
(seq)¶ Groups values of
(key, value)
pairs. May think of it likedict()
but collecting collisions:group_values(keep(r'^(\w+)=(.+)', sys.argv))

partition
(n, [step, ]seq)¶ 
ipartition
(n, [step, ]seq)¶ Returns a list of lists of
n
items each, at offsetsstep
apart. Ifstep
is not supplied, defaults ton
, i.e. the partitions do not overlap. Returns only full lengthn
partitions, in case there are not enough elements for last partition they are ignored.Most common use is deflattening data:
# Make a dict from flat list of pairs dict(ipartition(2, flat_list_of_pairs)) # Structure user credentials {id: (name, password) for id, name, password in ipartition(3, users)}
A three argument variant of
partition()
can be used to process sequence items in context of their neighbors:# Smooth data by averaging out with a sliding window [sum(window) / n for window in ipartition(n, 1, data_points)]
Also look at
pairwise()
for similar use. Other use ofpartition()
is processing sequence of data elements or jobs in chunks, but take a look atchunks()
for that.

chunks
(n, [step, ]seq)¶ 
ichunks
(n, [step, ]seq)¶ Returns a list of lists like
partition()
, but may include partitions with fewer thann
items at the end:chunks(2, 'abcde') # > ['ab', 'cd', 'e']) chunks(2, 4, 'abcde') # > ['ab', 'e'])
Handy for batch processing.
Data handling¶

distinct
(seq, key=identity)¶ 
idistinct
(seq, key=identity)¶ Returns the given sequence with duplicates removed. Preserves order. If
key
is supplied then distinguishes values by comparing their keys.Note
Elements of a sequence or their keys should be hashable.

with_prev
(seq, fill=None)¶ Returns an iterator of a pair of each item with one preceding it. Yields fill or None as preceding element for first item.
Great for getting rid of clunky
prev
housekeeping in for loops. This way one can indent first line of each paragraph while printing text:for line, prev in with_prev(text.splitlines()): if not prev: print ' ', print line
Use
pairwise()
to iterate only on full pairs.

with_next
(seq, fill=None)¶ Returns an iterator of a pair of each item with one next to it. Yields fill or None as next element for last item. See also
with_prev()
andpairwise()
.

pairwise
(seq)¶ Yields pairs of items in
seq
like(item0, item1), (item1, item2), ...
. A great way to process sequence items in a context of each neighbor:# Check if seq is nondescending all(left <= right for left, right in pairwise(seq))

count_by
(f, seq)¶ Counts numbers of occurrences of values of
f
on elements ofseq
. Returnsdefaultdict(int)
of counts.Calculating a histogram is one common use:
# Get a length histogram of given words count_by(len, words)

count_reps
(seq)¶ Counts number of repetitions of each value in
seq
. Returnsdefaultdict(int)
of counts. This is faster and shorter alternative tocount_by(identity, ...)

reductions
(f, seq[, acc])¶ 
ireductions
(f, seq[, acc])¶ Returns a sequence of the intermediate values of the reduction of
seq
byf
. In other words it yields a sequence like:reduce(f, seq[:1], [acc]), reduce(f, seq[:2], [acc]), ...
You can use
sums()
orisums()
for a common use of getting list of partial sums.

sums
(seq[, acc])¶ 
isums
(seq[, acc])¶ Same as
reductions()
orireductions()
with reduce function fixed to addition.Find out which straw will break camels back:
first(i for i, total in enumerate(isums(straw_weights)) if total > camel_toughness)