Welcome to funcy documentation!¶
Funcy is designed to be a layer of functional tools over python.
Special topics:
Overview¶
Just import stuff from funcy to make things happen:
from funcy import whatever, you, need
Merge collections of same type (works for dicts, sets, lists, tuples, iterators and even strings):
merge(coll1, coll2, coll3, ...)
join(colls)
merge_with(sum, dict1, dict2, ...)
Walk through collection, creating its transform (like map but preserves type):
walk(str.upper, {'a', 'b'}) # {'A', 'B'}
walk(reversed, {'a': 1, 'b': 2}) # {1: 'a', 2: 'b'}
walk_keys(double, {'a': 1, 'b': 2}) # {'aa': 1, 'bb': 2}
walk_values(inc, {'a': 1, 'b': 2}) # {'a': 2, 'b': 3}
Select a part of collection:
select(even, {1,2,3,10,20}) # {2,10,20}
select(r'^a', ('a','b','ab','ba')) # ('a','ab')
select_keys(callable, {str: '', None: None}) # {str: ''}
compact({2, None, 1, 0}) # {1,2}
Manipulate sequences:
take(4, iterate(double, 1)) # [1, 2, 4, 8]
first(drop(3, count(10))) # 13
remove(even, [1, 2, 3]) # [1, 3]
concat([1, 2], [5, 6]) # [1, 2, 5, 6]
cat(map(range, range(4))) # [0, 0, 1, 0, 1, 2]
mapcat(range, range(4)) # same
flatten(nested_structure) # flat_list
distinct('abacbdd') # list('abcd')
split(odd, range(5)) # ([1, 3], [0, 2, 4])
split_at(2, range(5)) # ([0, 1], [2, 3, 4])
group_by(mod3, range(5)) # {0: [0, 3], 1: [1, 4], 2: [2]}
partition(2, range(5)) # [[0, 1], [2, 3]]
chunks(2, range(5)) # [[0, 1], [2, 3], [4]]
pairwise(range(5)) # iter: [0, 1], [1, 2], ...
And functions:
partial(add, 1) # inc
curry(add)(1)(2) # 3
compose(inc, double)(10) # 21
complement(even) # odd
all_fn(isa(int), even) # is_even_int
one_third = rpartial(operator.div, 3.0)
has_suffix = rcurry(str.endswith)
Create decorators easily:
@decorator
def log(call):
print call._func.__name__, call._args
return call()
Abstract control flow:
walk_values(silent(int), {'a': '1', 'b': 'no'})
# => {'a': 1, 'b': None}
@once
def initialize():
"..."
with suppress(OSError):
os.remove('some.file')
@ignore(ErrorRateExceeded)
@limit_error_rate(fails=5, timeout=60)
@retry(tries=2, errors=(HttpError, ServiceDown))
def some_unreliable_action(...):
"..."
class MyUser(AbstractBaseUser):
@cached_property
def public_phones(self):
return self.phones.filter(public=True)
Ease debugging:
squares = {tap(x, 'x'): tap(x * x, 'x^2') for x in [3, 4]}
# x: 3
# x^2: 9
# ...
@print_exits
def some_func(...):
"..."
@log_calls(log.info, errors=False)
@log_errors(log.exception)
def some_suspicious_function(...):
"..."
with print_durations('Creating models'):
Model.objects.create(...)
# ...
# 10.2 ms in Creating models
Extended function semantics¶
Many of funcy functions expecting predicate or mapping function as an argument can take something uncallable instead of it with semantics described in this table:
f passed | Function | Predicate |
---|---|---|
None |
identity |
bool |
string | re_finder(f) |
re_tester(f) |
int or slice | itemgetter(f) |
itemgetter(f) |
mapping | lambda x: f[x] |
lambda x: f[x] |
set | lambda x: x in f |
lambda x: x in f |
Supporting functions¶
Here is a full list of functions supporting extended function semantics:
Group | Functions |
---|---|
Sequence transformation | map() , imap() , keep() , ikeep() , mapcat() , imapcat() |
Sequence filtering | filter() , ifilter() , remove() , iremove() , distinct() , idistinct() |
Sequence splitting | dropwhile() , takewhile() , split() , split_by() |
Sequence chunking | group_by() , count_by() , partition_by() , ipartition_by() |
Collection transformation | walk() , walk_keys() , walk_values() |
Collection filtering | select() , select_keys() , select_values() |
Content tests | all() , any() , none() , one() , some() , is_distinct() |
Function logic | all_fn() , any_fn() , none_fn() , one_fn() , some_fn() |
Function tools | compose() , rcompose() , complement() , juxt() , ijuxt() |
Python 3 support¶
Funcy works with python 3 as of version 0.9. However, it has slightly different interface. It follows python 3 convention of “iterator by default” for utilities like map()
, filter()
and such. When funcy has two versions of utility (list and iterator) they are named like keep()
and ikeep()
in python 2 and lkeep()
and keep()
in python 3. You can look up a full table of differently named functions below.
Writing cross-python code¶
You can do that two ways: writing python 2 code that works in python 3 or vice versa. You can import python 2 or 3 style functions from funcy.py2
or funcy.py3
:
from funcy.py2 import whatever, you, need
# write python 2 style code here
from funcy.py3 import whatever, you, need
# write python 3 style code here
You can even import map()
, imap()
, filter()
, ifilter()
, zip()
and izip()
.
Full table of python dependent function names¶
Contents:
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 list-returning function name.
When working with sequences, see also itertools
standard module. Funcy reexports and aliases some functions from it.
Generate¶
-
repeat
(elem[, n])¶ Makes an iterator returning
elem
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 ofelem
you can use:[elem] * n # or (elem,) * 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 BASIC-style 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 freshly-created 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 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 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 first item in sequence. Returns
None
if 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 second item in 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 last item in sequence. Returns
None
if 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 sequence, returning iterator starting just after it. A shortcut for
drop(1, seq)
.
-
butlast
(seq)¶ Returns iterator of all elements of a 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)¶ Returns concatenation of 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 two-level 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 whitespace-only 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 non-empty 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 without
items
specified, 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 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:
absolute, relative = split(re_tester(r'^http://'), urls)
-
split_at
(n, seq)¶ -
isplit_at
(n, seq)¶ Splits sequence at given position, returning a tuple
take(n, seq), list(drop(n, seq))
.
-
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)¶ Returns an iterator of
seq
elements as long aspred
for each of them is true. Stop 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()
. Returns iterator skipping elements of given sequence whilepred
is true and then yielding the rest of it:# Skip leading whitespace-only 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 length-n
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 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 non-descending all(left <= right for left, right in pairwise(seq))
-
count_by
(f, seq)¶ Counts number 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)
-
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)
Collections¶
Unite¶
-
merge
(*colls)¶ Merges several collections of same type into one: dicts, sets, lists, tuples, iterators or strings. For dicts values of later dicts override values of former ones with same keys.
Can be used in variety of ways, but merging dicts is probably most common:
def utility(**options): defaults = {...} options = merge(defaults, options) ...
If you merge sequences and don’t need to preserve collection type, then use
concat()
oriconcat()
instead.
Transform and select¶
All functions in this section support Extended function semantics.
-
walk
(f, coll)¶ Returns collection of same type as
coll
consisting of its elements mapped with given function:walk(inc, {1, 2, 3}) # -> {2, 3, 4} walk(inc, (1, 2, 3)) # -> (2, 3, 4)
When walking dict,
(key, value)
pairs are mapped, i.e. this linesflip()
dict:swap = lambda (k, v): (v, k) walk(swap, {1: 10, 2: 20})
walk()
works with strings too:walk(lambda x: x * 2, 'ABC') # -> 'AABBCC' walk(compose(str, ord), 'ABC') # -> '656667'
One should probably use
map()
orimap()
when doesn’t need to preserve collection type.
-
walk_keys
(f, coll)¶ Walks keys of
coll
, mapping them with given function. Works with mappings and collections of pairs:walk_keys(str.upper, {'a': 1, 'b': 2}) # {'A': 1, 'B': 2} walk_keys(int, json.loads(some_dict)) # restore key type lost in translation
Important to note that it preserves collection type whenever this is simple
dict
,defaultdict
,OrderedDict
or any other mapping class or a collection of pairs.
-
walk_values
(f, coll)¶ Walks values of
coll
, mapping them with given function. Works with mappings and collections of pairs.Common use is to process values somehow:
clean_values = walk_values(int, form_values) sorted_groups = walk_values(sorted, groups)
Hint: you can use
partial(sorted, key=...)
instead ofsorted()
to sort in non-default way.Note that
walk_values()
has special handling fordefaultdicts
. It constructs new one with values mapped the same as for ordinary dict, but a default factory of newdefaultdict
would be a composition off
and old default factory:d = defaultdict(lambda: 'default', a='hi', b='bye') walk_values(str.upper, d) # -> defaultdict(lambda: 'DEFAULT', a='HI', b='BYE')
-
select
(pred, coll)¶ Filters elements of
coll
bypred
constructing collection of same type. When filtering a dictpred
receives(key, value)
pairs. Seeselect_keys()
andselect_values()
to filter it by keys or values respectively:select(even, {1, 2, 3, 10, 20}) # -> {2, 10, 20} select(lambda (k, v): k == v, {1: 1, 2: 3}) # -> {1: 1}
-
select_keys
(pred, coll)¶ Select part of a dict or a collection of pairs with keys passing given predicate.
This way a public part of instance attributes dictionary could be selected:
is_public = complement(re_tester('^_')) public = select_keys(is_public, instance.__dict__)
-
select_values
(pred, coll)¶ Select part of a dict or a collection of pairs with values passing given predicate.
Strip falsy values from dict:
select_values(bool, some_dict)
-
compact
(coll)¶ Removes falsy values from given collection. When compacting a dict all keys with falsy values are trashed.
Extract integer data from request:
compact(walk_values(silent(int), request_dict))
Dict utils¶
-
merge_with
(f, *dicts)¶ -
join_with
(f, dicts)¶ Merge several dicts combining values for same key with given function:
merge_with(list, {1: 1}, {1: 10, 2: 2}) # -> {1: [1, 10], 2: [2]} merge_with(sum, {1: 1}, {1: 10, 2: 2}) # -> {1: 11, 2: 2} join_with(first, ({n % 3: n} for n in range(100, 110))) # -> {0: 102, 1: 100, 2: 101}
-
zipdict
(keys, vals)¶ Returns a dict with the
keys
mapped to the correspondingvals
. Stops pairing on shorter sequence end:zipdict('abcd', range(4)) # -> {'a': 0, 'b': 1, 'c': 2, 'd': 3} zipdict('abc', count()) # -> {'a': 0, 'b': 1, 'c': 2}
-
flip
(mapping)¶ Flip passed dict swapping its keys and values. Also works for sequences of pairs. Preserves collection type:
flip(OrderedDict(['aA', 'bB'])) # -> OrderedDict([('A', 'a'), ('B', 'b')])
-
project
(mapping, keys)¶ Returns a dict containing only those entries in
mapping
whose key is inkeys
.Most useful to shrink some common data or options to predefined subset. One particular case is constructing a dict of used variables:
merge(project(__builtins__, names), project(globals(), names))
-
izip_values
(*dicts)¶ Yields tuples of corresponding values of given dicts. Skips any keys not present in all of the dicts. Comes in handy when comparing two or more dicts:
max_change = max(abs(x - y) for x, y in izip_values(items, old_items))
-
izip_dicts
(*dicts)¶ Yields tuples like
(key, value1, value2, ...)
for each common key of all given dicts. A neat way to process several dicts at once:changed_items = [id for id, (new, old) in izip_dicts(items, old_items) if abs(new - old) >= PRECISION] lines = {id: cnt * price for id, (cnt, price) in izip_dicts(amounts, prices)}
See also
izip_values()
.
-
get_in
(coll, path, default=None)¶ Returns the value corresponding to the path in the dictionary:
get_in({"a": {"b": 42}}, ["a", "b"]) # -> 42 get_in({"a": {"b": 42}}, ["c"], "foo") # -> "foo"
-
set_in
(coll, path, value)¶ Creates a dictionary with a
value
set at specifiedpath
. Original collection is not changed:set_in({"a": {"b": 42}}, ["a", "b"], 10) # -> {"a": {"b": 10}} set_in({"a": {"b": 42}}, ["a", "c"], 10) # -> {"a": {"b": 42, "c": 10}}
-
update_in
(coll, path, update, default=None)¶ Creates a dictionary with a value at specified
path
updated:update_in({"a": {}}, ["a", "cnt"], inc, default=0) # -> {"a": {"cnt": 1}}
Data manipulation¶
-
where
(mappings, **cond)¶ -
iwhere
(mappings, **cond)¶ Looks through each value in given sequence of dicts, returning a list or an iterator of all the dicts that contain all of the key-value pairs in
cond
:where(plays, author="Shakespeare", year=1611) # => [{"title": "Cymbeline", "author": "Shakespeare", "year": 1611}, # {"title": "The Tempest", "author": "Shakespeare", "year": 1611}]
Iterator version could be used for efficiency or when you don’t need the whole list. E.g. you are looking for the first match:
first(iwhere(plays, author=”Shakespeare”)) # => {“title”: “The Two Gentlemen of Verona”, ...}
-
pluck
(key, mappings)¶ -
ipluck
(key, mappings)¶ Returns a list or an iterator of values for
key
in each mapping in given sequence. Essentially a shortcut for:map(operator.itemgetter(key), mappings)
-
pluck_attr
(attr, objects)¶ -
ipluck_attr
(attr, objects)¶ Returns a list or an iterator of values for
attr
in each object in given sequence. Essentially a shortcut for:map(operator.attrgetter(attr), objects)
Useful when dealing with collections of ORM objects:
users = User.query.all() ids = pluck_attr('id', users)
Content tests¶
-
is_distinct
(coll, key=identity)¶ Checks if all elements in collection are different:
assert is_distinct(field_names), "All fields should be named differently"
Uses
key
to differentiate values. This way one can check if all first letters ofwords
are different:is_distinct(words, key=0)
-
all
([pred, ]seq)¶ Checks if
pred
holds every element in aseq
. Ifpred
is omitted checks if all elements ofseq
is true (which is the same as in built-inall()
):they_are_ints = all(is_instance(n, int) for n in seq) they_are_even = all(even, seq)
Note that, first example could be rewritten using
isa()
like this:they_are_ints = all(isa(int), seq)
-
any
([pred, ]seq)¶ Returns
True
ifpred
holds for any item in given sequence. Ifpred
is omitted checks if any element ofseq
is true.Check if there is a needle in haystack, using extended predicate semantics:
any(r'needle', haystack_strings)
-
none
([pred, ]seq)¶ Checks if none of items in given sequence pass
pred
or true ifpred
is omitted.Just a stylish way to write
not any(...)
:assert none(' ' in name for name in names), "Spaces in names not allowed"
-
one
([pred, ]seq)¶ Returns true if exactly one of items in
seq
passespred
. Cheks for boolean true ifpred
is omitted.
-
some
([pred, ]seq)¶ Finds first item in
seq
passingpred
or first that is true ifpred
is omitted.
Low-level helpers¶
-
empty
(coll)¶ Returns an empty collection of the same type as
coll
.
-
iteritems
(coll)¶ Returns an iterator of items of a
coll
. This meanskey, value
pairs for any dictionaries:list(iteritems({1, 2, 42})) # -> [1, 42, 2] list(iteritems({'a': 1})) # -> [('a', 1)]
-
itervalues
(coll)¶ Returns an iterator of values of a
coll
. This means values for any dictionaries and just elements for other collections:list(itervalues({1, 2, 42})) # -> [1, 42, 2] list(itervalues({'a': 1})) # -> [1]
Functions¶
-
identity
(x)¶ Returns its argument.
-
constantly
(x)¶ Returns function accepting any args, but always returning
x
.
-
caller
(*args, **kwargs)¶ Returns function calling its argument with passed arguments.
-
partial
(func, *args, **kwargs)¶ Returns partial application of
func
. A re-export offunctools.partial()
. Can be used in a variety of ways. DSLs is one of them:field = dict json_field = partial(field, json=True)
-
rpartial
(func, *args)¶ Partially applies last arguments in
func
:from operator import div one_third = rpartial(div, 3.0)
Arguments are passed to
func
in the same order as they came torpartial()
:separate_a_word = rpartial(str.split, ' ', 1)
-
func_partial
(func, *args, **kwargs)¶ Like
partial()
but returns a real function. Which is useful when, for example, you want to create a method of it:setattr(self, 'get_%s_display' % field.name, func_partial(_get_FIELD_display, field))
Note: use
partial()
if you are ok to get callable object instead of function as it’s faster.
-
curry
(func[, n])¶ Curries function. For example, given function of two arguments
f(a, b)
returns function:lambda a: lambda b: f(a, b)
Handy to make a partial factory:
make_tester = curry(re_test) is_word = make_tester(r'^\w+$') is_int = make_tester(r'^[1-9]\d*$')
But see
re_tester()
if you really need this.
-
rcurry
(func[, n])¶ Curries function from last argument to first:
has_suffix = rcurry(str.endswith) filter(has_suffix("ce"), ["nice", "cold", "ice"]) # -> ["nice", "ice"]
Can fix number of arguments when it’s ambiguous:
to_power = rcurry(pow, 2) # curry 2 first args in reverse order to_square = to_power(2) to_cube = to_power(3)
-
autocurry
(func[, n])¶ Constructs a version of
func
returning it’s partial application if insufficient arguments passed:def remainder(what, by): return what % by rem = autocurry(remainder) assert rem(10, 3) == rem(10)(3) == rem()(10, 3) == 1 assert map(rem(by=3), range(5)) == [0, 1, 2, 0, 1]
Can clean your code a bit when
partial()
makes it too cluttered.
-
compose
(*fs)¶ Returns composition of functions:
extract_int = compose(int, r'\d+')
Supports Extended function semantics.
-
rcompose
(*fs)¶ Returns composition of functions, with functions called from left to right. Designed to facilitate transducer-like pipelines:
# Note the use of iterator function variants everywhere process = rcompose( partial(iremove, is_useless), partial(imap, process_row), partial(ichunks, 100) ) for chunk in process(data): write_chunk_to_db(chunk)
Supports Extended function semantics.
-
juxt
(*fs)¶ -
ijuxt
(*fs)¶ Takes several functions and returns a new function that is the juxtaposition of those. The resulting function takes a variable number of arguments, and returns a list or iterator containing the result of applying each function to the arguments.
-
iffy
([pred, ]action[, default=identity])¶ Returns function, which conditionally, depending on
pred
, appliesaction
ordefault
. Ifdefault
is not callable then it is returned as is from resulting function. E.g. this will call all callable values leaving rest of them as is:map(iffy(callable, caller()), values)
Common use it to deal with messy data:
dirty_data = ['hello', None, 'bye'] map(iffy(len), dirty_data) # => [5, None, 3] map(iffy(isa(str), len, 0), dirty_data) # => [5, 0, 3], also safer
Function logic¶
This family of functions supports creating predicates from other predicates and regular expressions.
-
complement
(pred)¶ Constructs a predicate of passed function, i.e. a function returning a boolean opposite of original function:
is_private = re_tester(r'^_') is_public = complement(is_private) # or just is_public = complement(r'^_')
-
all_fn
(*fs)¶ -
any_fn
(*fs)¶ -
none_fn
(*fs)¶ -
one_fn
(*fs)¶ Construct a predicates returning
True
when all, any, none or exactly one offs
returnTrue
. Support short-circuit behavior.is_even_int = all_fn(isa(int), even)
-
some_fn
(*fs)¶ Constructs function calling
fs
one by one and returning first true result.Enables creating functions by short-circuiting several behaviours:
get_amount = some_fn( lambda s: 4 if 'set of' in s else None, r'(\d+) wheels?', compose({'one': 1, 'two': 2, 'pair': 2}, r'(\w+) wheels?') )
If you wonder how on Earth one can
compose()
dict and string see Extended function semantics.
Decorators¶
-
@
decorator
¶ Transforms a flat wrapper into a decorator with or without arguments.
@decorator
passes specialcall
object as a first argument to a wrapper. A resulting decorator will preserve function module, name and docstring. It also adds__wrapped__
attribute referring to wrapped function and__original__
attribute referring to innermost wrapped one.Here is a simple logging decorator:
@decorator def log(call): print call._func.__name__, call._args, call._kwargs return call()
call
object also supports by name arg introspection and passing additional arguments to decorated function:@decorator def with_phone(call): # call.request gets actual request value upon function call request = call.request # ... phone = Phone.objects.get(number=request.GET['phone']) # phone arg is added to *args passed to decorated function return call(phone) @with_phone def some_view(request, phone): # ... some code using phone return # ...
A better practice would be adding keyword argument not positional. This makes such decorators more composable:
@decorator def with_phone(call): # ... return call(phone=phone) @decorator def with_user(call): # ... return call(user=user) @with_phone @with_user def some_view(request, phone=None, user=None): # ... return # ...
If a function wrapped with
@decorator
has arguments other thancall
, then decorator with arguments is created:@decorator def joining(call, sep): return sep.join(call())
You can see more examples in
flow
anddebug
submodules source code.
-
@
contextmanager
(func)¶ A decorator helping to create context managers. Resulting functions also behave as decorators.
A simple example:
@contextmanager def tag(name): print "<%s>" % name, yield print "</%s>" % name with tag("h1"): print "foo", # -> <h1> foo </h1>
Using as decorator:
@tag('strong') def shout(text): print text.upper() shout('hooray') # -> <strong> HOORAY </strong>
-
@
wraps
(wrapped[, assigned][, updated])¶ An utility to pass function metadata from wrapped function to a wrapper. Copies all function attributes including
__name__
,__module__
and__doc__
.In addition adds
__wrapped__
attribute referring to the wrapped function and__original__
attribute referring to innermost wrapped one.Mostly used to create decorators:
def some_decorator(func): @wraps(func) def wrapper(*args, **kwargs): do_something(*args, **kwargs) return func(*args, **kwargs)
But see also
decorator()
for that.
-
unwrap
(func)¶ Get the object wrapped by
func
.Follows the chain of
__wrapped__
attributes returning the last object in the chain.
-
class
ContextDecorator
¶ A base class or mixin that enables context managers to work as decorators.
Flow¶
-
@
silent
¶ Ignore all real exceptions (descendants of
Exception
). Handy for cleaning data such as user input:brand_id = silent(int)(request.GET['brand_id']) ids = keep(silent(int), request.GET.getlist('id'))
And in data import/transform:
get_greeting = compose(silent(string.lower), re_finder(r'(\w+)!')) map(get_greeting, ['a!', ' B!', 'c.']) # -> ['a', 'b', None]
Note
Avoid silencing non-primitive functions, use
ignore()
instead and even then be careful not to swallow exceptions unintentionally.
-
@
ignore
(errors, default=None)¶ Same as
silent()
, but able to specifyerrors
to catch anddefault
to return in case of error caught.errors
can either be exception class or tuple of them.
-
suppress
(*errors)¶ A context manager which suppresses given exceptions under its scope:
with suppress(HttpError): # Assume this request can fail, and we are ok with it make_http_request()
-
@
once
¶ -
@
once_per_args
¶ -
@
once_per
(*argnames)¶ Call function only once, once for every combination of values of its arguments or once for every combination of given arguments. Thread safe. Handy for various initialization purposes:
# Global initialization @once def initialize_cache(): conn = some.Connection(...) # ... set up everything # Per argument initialization @once_per_args def initialize_language(lang): conf = load_language_conf(lang) # ... set up language # Setup each class once class SomeManager(Manager): @once_per('cls') def contribute_to_class(self, cls): pre_save.connect(self._pre_save, sender=cls) # ... set up signals, no dups
-
raiser
(exception_or_class=Exception, *args, **kwargs)¶ Constructs function that raises given exception with given arguments on any invocation.
-
@
retry
(tries, errors=Exception, timeout=0)¶ Every call of decorated function retried up to
tries
times if any subclass oferrors
occurs (could be exception class or a tuple of them). There will be delay intimeout
seconds between tries.A common use is to wrap some unreliable action:
@retry(3, errors=HttpError) def download_image(url): # ... make http request return image
You can pass callable as
timeout
to achieve exponential delays or other complex behavior:@retry(3, errors=HttpError, timeout=lambda a: 2 ** a) def download_image(url): # ... make http request return image
-
fallback
(*approaches)¶ Tries several approaches until one works. Each approach is either callable or a tuple
(callable, errors)
, where errors is an exception class or a tuple of classes, which signal to fall back to next approach. Iferrors
is not supplied then fall back is done for anyException
:fallback( (partial(send_mail, ADMIN_EMAIL, message), SMTPException), partial(log.error, message), raiser(FeedbackError, "Unable to notify admin") )
-
limit_error_rate
(fails, timeout, exception=ErrorRateExceeded)¶ If function fails to complete
fails
times in a row, calls to it will be intercepted fortimeout
withexception
raised instead. A clean way to short-circuit function taking too long to fail:@limit_error_rate(fails=5, timeout=60, exception=RequestError('Temporary unavailable')) def do_request(query): # ... make a http request return data
Can be combined with
ignore()
to silently stop trying for a while:@ignore(ErrorRateExceeded, default={'id': None, 'name': 'Unknown'}) @limit_error_rate(fails=5, timeout=60) def get_user(id): # ... make a http request return data
-
@
collecting
¶ Transforms generator or other iterator returning function into list returning one.
Handy to prevent quirky iterator-returning properties:
@property @collecting def path_up(self): node = self while node: yield node node = node.parent
Also makes list constructing functions beautifully yielding.
-
@
joining
(sep)¶ Wraps common python idiom “collect then join” into a decorator. Transforms generator or alike into function, returning string of joined results. Automatically converts all elements to separator type for convenience.
Goes well with generators with some ad-hoc logic within:
@joining(', ') def car_desc(self): yield self.year_made if self.engine_volume: yield '%s cc' % self.engine_volume if self.transmission: yield self.get_transmission_display() if self.gear: yield self.get_gear_display() # ...
Use
unicode
separator to get unicode result:@joining(u', ') def car_desc(self): yield self.year_made # ...
See also
str_join()
.
String utils¶
-
re_find
(regex, s, flags=0)¶ Finds
regex
ins
, returning the match in most simple form guessed by captures in given regular expression:Captures Return value no captures a matched string single positional capture a substring matched by capture only positional captures a tuple of substrings for captures only named captures a dict of substrings for captures mixed pos/named captures a match object Returns
None
on mismatch.# Find first number in a line silent(int)(re_find(r'\d+', line)) # Find number of men in a line re_find(r'(\d+) m[ae]n', line) # Parse uri into nice dict re_find(r'^/post/(?P<id>\d+)/(?P<action>\w+)$', uri)
-
re_test
(regex, s, flags=0)¶ Tests whether
regex
can be found ins
.
-
re_all
(regex, s, flags=0)¶ -
re_iter
(regex, s, flags=0)¶ Returns a list or iterator of all matches of
regex
ins
. Matches are presented in most simple form possible, see table inre_find()
docs.# A fast and dirty way to parse ini section into dict dict(re_iter('(\w+)=(\w+)', ini_text))
-
re_finder
(regex, flags=0)¶ Returns a function that calls
re_find()
for it’s sole argument. It’s main purpose is quickly constructing mapper functions formap()
and friends.See also Extended function semantics.
-
re_tester
(regex, flags=0)¶ Returns a function that calls
re_test()
for it’s sole argument. Aimed at quick construction of predicates for use infilter()
and friends.See also Extended function semantics.
-
str_join
([sep="", ]seq)¶ Joins sequence by
sep
. Same assep.join(seq)
, but forcefully converts all elements to separator type,str
by default.See also
joining()
.
-
cut_prefix
(s, prefix)¶ Cuts prefix from given string if it’s present.
-
cut_suffix
(s, suffix)¶ Cuts suffix from given string if it’s present.
Calculation¶
-
@
memoize
¶ Memoizes decorated function results, trading memory for performance. Can skip memoization for failed calculation attempts:
@memoize def ip_to_city(ip): try: return request_city_from_slow_service(ip) except NotFound: return None # return None and memoize it except Timeout: raise memoize.skip # return None, but don't memoize it
Use
raise memoize.skip(some_value)
to make function returnsome_value
on fail instead ofNone
.
-
@
make_lookuper
¶ As
memoize()
, but with prefilled memory. Decorated function should return fully filled memory, which should be a dict or a sequence of pairs. Resulting function will raiseLookupError
for any argument missing in it:@make_lookuper def city_location(): return {row['city']: row['location'] for row in fetch_city_locations()}
If decorated function has arguments then separate lookuper with its own lookup table is created for each combination of arguments. This can be used to make lookup tables on demand:
@make_lookuper def function_lookup(f): return {x: f(x) for x in range(100)} fast_sin = function_lookup(math.sin) fast_cos = function_lookup(math.cos)
Or load some resources, memoize them and use as a function:
@make_lookuper def translate(lang): return make_list_of_pairs(load_translation_file(lang)) russian_phrases = map(translate('ru'), english_phrases)
-
@
silent_lookuper
¶ Same as
make_lookuper()
, but returnsNone
on memory miss.
-
@
cache
(timeout)¶ Same as
memoize()
, but doesn’t use cached results older thantimeout
. It can be either number of seconds ordatetime.timedelta
. Also, doesn’t support skipping.
Type testing¶
-
isa
(*types)¶ Returns function checking if it’s argument is of any of given
types
.Split labels from ids:
labels, ids = split_by(isa(str), values)
-
is_mapping
(value)¶ -
is_set
(value)¶ -
is_list
(value)¶ -
is_tuple
(value)¶ -
is_seq
(value)¶ -
is_iter
(value)¶ These functions check if value is
Mapping
,Set
,list
,tuple
,Sequence
or iterator respectively.
-
is_seqcoll
(value)¶ Checks if
value
is a list or a tuple, which are both sequences and collections.
-
is_seqcont
(value)¶ Checks if
value
is a list, a tuple or an iterator, which are sequential containers. It can be used to distinguish between value and multiple values in dual-interface functions:def add_to_selection(view, region): if is_seqcont(region): # A sequence of regions view.sel().add_all(region) else: view.sel().add(region)
-
iterable
(value)¶ Tests if
value
is iterable.
Objects¶
-
@
cached_property
¶ Creates a property caching its result. One can rewrite cached value simply by assigning property. And clear cache by deleting it.
A great way to lazily attach some data to an object:
class MyUser(AbstractBaseUser): @cached_property def public_phones(self): return list(self.phones.filter(confirmed=True, public=True))
-
@monkey(cls_or_module, name=None)
Monkey-patches class or module by adding decorated function or property to it named
name
or the same as decorated function. Saves overwritten method tooriginal
attribute of decorated function for a kind of inheritance:# A simple caching of all get requests, # even for models for which you can't easily change Manager @monkey(QuerySet) def get(self, *args, **kwargs): if not args and list(kwargs) == ['pk']: cache_key = '%s:%d' % (self.model, kwargs['pk']) result = cache.get(cache_key) if result is None: result = get.original(self, *args, **kwargs) cache.set(cache_key, result) return result else: return get.original(self, *args, **kwargs)
Debugging¶
-
tap
(value, label=None)¶ Prints value and then returns it. Useful to tap into some functional pipeline for debugging:
fields = (f for f in fields_for(category) if section in tap(tap(f).sections)) # ... do something with fields
If
label
is specified then it’s printed before corresponding value:squares = {tap(x, 'x'): tap(x * x, 'x^2') for x in [3, 4]} # x: 3 # x^2: 9 # x: 4 # x^2: 16 # => {3: 9, 4: 16}
-
@
log_calls
(print_func, errors=True, stack=True)¶ -
@
print_calls
(errors=True, stack=True)¶ Will log or print all function calls, including arguments, results and raised exceptions. Can be used as decorator or tapped into call expression:
sorted_fields = sorted(fields, key=print_calls(lambda f: f.order))
If
errors
is set toFalse
then exceptions are not logged. This could be used to separate channels for normal and error logging:@log_calls(log.info, errors=False) @log_errors(log.exception) def some_suspicious_function(...): # ... return result
print_calls()
always prints everything, including error stack traces.
-
@
log_enters
(print_func)¶ -
@
print_enters
¶ -
@
log_exits
(print_func, errors=True, stack=True)¶ -
@
print_exits
(errors=True, stack=True)¶ Will log or print every time execution enters or exits the function. Should be used same way as
log_calls()
andprint_calls()
when you need to track only one event per function call.
-
@
log_errors
(print_func, label=None, stack=True)¶ -
@
print_errors
(label=None, stack=True)¶ Will log or print all function errors providing function arguments causing them. If
stack
is set toFalse
then each error is reported with simple one line message.Can be combined with
silent()
orignore()
to trace occasionally misbehaving function:@silent @log_errors(logging.warning) def guess_user_id(username): initial = first_guess(username) # ...
Can also be used as context decorator:
with print_errors('initialization', stack=False): load_this() load_that() # ... # SomeException: a bad thing raised in initialization
-
@
log_durations
(print_func, label=None)¶ -
@
print_durations
(label=None)¶ Will time each function call and log or print its duration:
@log_durations(logging.info) def do_hard_work(n): samples = range(n) # ... # 121 ms in do_hard_work(10) # 143 ms in do_hard_work(11) # ...
A block of code could be timed with a help of context manager:
with print_durations('Creating models'): Model.objects.create(...) # ... # 10.2 ms in Creating models
-
log_iter_durations
(seq, print_func, label=None)¶ -
print_iter_durations
(seq, label=None)¶ Wraps iterable
seq
into generator logging duration of processing of each item:for item in print_iter_durations(seq, label='hard work'): do_smth(item) # 121 ms in iteration 0 of hard work # 143 ms in iteration 1 of hard work # ...
Primitives¶
-
isnone(x):
Checks if
x
isNone
. Handy with filtering functions:remove(isnone, list_of_dirty_data)
Plays nice with
silent()
, which returnsNone
on fail:remove(isnone, imap(silent(int), strings_with_numbers))
Note that it’s usually simpler to use
keep()
orcompact()
if you don’t need to distinguish betweenNone
and other falsy values.
-
notnone(x):
Checks if
x
is notNone
. A shortcut forcomplement(isnone)
meant to be used whenbool
is not specific enough. Compare:select_values(notnone, data_dict) # removes None values compact(data_dict) # removes all falsy values
-
inc(x):
Increments its argument by 1.
-
dec(x):
Decrements its argument by 1.
-
even(x):
Checks if
x
is even.
-
odd(x):
Checks if
x
is odd.
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