Funcy is designed to be a layer of functional tools over python.
Special topics:
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.
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().
| Python 2 / list | Python 2 / iterator | Python 3 / list | Python 3 / iterator |
|---|---|---|---|
| map() | imap() | lmap() | map() |
| filter() | ifilter() | lfilter() | filter() |
| zip() | izip() | lzip() | zip() |
| remove() | iremove() | lremove() | remove() |
| keep() | ikeep() | lkeep() | keep() |
| without() | iwithout() | lwithout() | without() |
| concat() | iconcat() | lconcat() | concat() |
| cat() | icat() | lcat() | cat() |
| flatten() | iflatten() | lflatten() | flatten() |
| mapcat() | imapcat() | lmapcat() | mapcat() |
| distinct() | idistinct() | ldistinct() | distinct() |
| split() | isplit() | lsplit() | split() |
| split_at() | isplit_at() | lsplit_at() | split_at() |
| split_by() | isplit_by() | lsplit_by() | split_by() |
| partition() | ipartition() | lpartition() | partition() |
| chunks() | ichunks() | lchunks() | chunks() |
| partition_by() | ipartition_by() | lpartition_by() | partition_by() |
| reductions() | ireductions() | lreductions() | reductions() |
| sums() | isums() | lsums() | sums() |
| juxt() | ijuxt() | ljuxt() | juxt() |
| where() | - | - | where() |
| pluck() | - | - | pluck() |
| invoke() | - | - | invoke() |
| - | izip_values() | - | zip_values() |
| - | izip_dicts() | - | zip_dicts() |
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 | Predicate | Mapping function |
|---|---|---|
| None | bool | identity |
| string | re_tester(f) | re_finder(f) |
| int or slice | itemgetter(f) | itemgetter(f) |
| mapping | f.get | f.get |
| set | lambda x: x in f | lambda x: x in f |
Here is a full list of functions supporting extended predicate/mapper 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(), complement(), juxt(), ijuxt() |
Contents:
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.
Makes an iterator returning elem for n times or indefinitely if n is omitted. repeat() simply repeat given value, when you need to reevaluate something repeatedly use repeatedly() instead.
When you just need a length n list or tuple of elem you can use:
[elem] * n
# or
(elem,) * n
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() or izip():
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 original itertools.count() documentation.
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)
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)
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)
This section provides some robust tools for sequence slicing. Consider Slicings or itertools.islice() for more generic cases.
Returns a list of the first n items in sequence, or all items if there are fewer than n.
take(3, [2, 3, 4, 5]) # [2, 3, 4]
take(3, count(5)) # [5, 6, 7]
take(3, 'ab') # ['a', 'b']
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
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() or imap(). See last example in iterate() for such example.
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))
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))
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.
Skips first item in sequence, returning iterator starting just after it. A shortcut for drop(1, seq).
Returns iterator of all elements of a sequence but last.
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)))
Concats several sequences into one. iconcat() returns an iterator yielding concatenation.
iconcat() is an alias for itertools.chain().
Returns concatenation of passed sequences. Useful when dealing with sequence of sequences, see concat() or iconcat() 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 for itertools.chain.from_iterable().
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, see is_seqcont() for further explanation.
See also cat() or icat() if you need to flatten strictly two-level sequence of sequences.
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.
Returns an iterator yielding elements of seq separated by sep.
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))
Most of functions in this section support Extended predicate/mapper semantics. Among other things it allows to rewrite examples using re_tester() and re_finder() tighter.
Return a list or an iterator of items of seq that result in false when passed to pred. The results of this functions complement results of standard filter() and ifilter().
A handy use is passing re_tester() result as pred. For example, this code removes any whitespace-only lines from list:
remove(re_tester('^\s+$'), lines)
Note, you can rewrite it shorter using extended predicate semantics:
remove('^\s+$', lines)
Maps seq with given function and then filters out falsy elements. Simply filters seq when f 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() and imap().
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
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)
Returns sequence without items specified, preserves order. Designed to work with a few items, 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.
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)
Splits sequence at given position, returning a tuple take(n, seq), list(drop(n, 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 iterator seq correctly:
split_by(bool, iter([-2, -1, 0, 1, 2]))
# [-2, -1], [0, 1, 2]
Returns an iterator of seq elements as long as pred 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))
This is a mirror of takewhile(). Returns iterator skipping elements of given sequence while pred is true and then yielding the rest of it:
# Skip leading whitespace-only lines
dropwhile(re_tester('^\s*$'), text_lines)
Returns a dict of the elements of seq keyed by the result of f on each element. The value at each key will be a list of the corresponding elements, in the order they appeared in seq.
group_by(len, ['a', 'ab', 'b'])
# -> {1: ['a', 'b'], 2: ['ab']}
One can use split_by() when grouping by boolean predicate. See also itertools.groupby().
Groups elements of seq having multiple keys each. Can be used to reverse grouping:
posts_by_tag = group_by_keys(attrgetter(tags), posts)
sentences_with_word = group_by_keys(str.split, sentences)
Returns a list of lists of n items each, at offsets step apart. If step is not supplied, defaults to n, 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 of partition() is processing sequence of data elements or jobs in chunks, but take a look at chunks() for that.
Returns a list of lists like partition(), but may include partitions with fewer than n items at the end:
chunks(2, 'abcde')
# -> ['ab', 'cd', 'e'])
chunks(2, 4, 'abcde')
# -> ['ab', 'e'])
Handy for batch processing.
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.
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
See also ipartition_by() and itertools.groupby() for chunking sequence by condition.
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))
Counts number of occurrences of values of f on elements of seq and returns a dict of counts.
Calculation a histogram is one common use:
# Get a length histogram of given words
count_by(len, words)
Returns a sequence of the intermediate values of the reduction of seq by f. In other words it yields a sequence like:
reduce(f, seq[:1], [acc]), reduce(f, seq[:2], [acc]), ...
You can use sums() or isums() for a common use of getting list of partial sums.
Same as reductions() or ireductions() 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)
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() or iconcat() instead.
Most of functions in this section support Extended predicate/mapper semantics.r
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 lines flip() 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() or imap() when doesn’t need to preserve collection type.
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.
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 of sorted() to sort in non-default way.
Filters elements of coll by pred constructing collection of same type. When filtering a dict pred receives (key, value) pairs. See select_keys() and select_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 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 part of a dict or a collection of pairs with values passing given predicate.
Strip falsy values from dict:
select_values(bool, some_dict)
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))
Returns a dict with the keys mapped to the corresponding vals. 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 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')])
Returns a dict containing only those entries in mapping whose key is in keys.
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))
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))
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().
Looks through each value in given sequence of dicts, returning a list 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}]
Returns list of values for key in each mapping in given sequence. Essentially a shortcut for:
map(operator.itemgetter(key), mappings)
Calls named method with given arguments for each object in objects and returns a list of results.
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 of words are different:
is_distinct(words, key=0)
Checks if pred holds every element in a seq. If pred is omitted checks if all elements of seq is true (which is the same as in built-in all()):
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)
Returns True if pred holds for any item in given sequence. If pred is omitted checks if any element of seq is true.
Check if there is a needle in haystack, using extended predicate semantics:
any(r'needle', haystack_strings)
Checks if none of items in given sequence pass pred or true if pred is omitted.
Just a stylish way to write not any(...):
assert none(' ' in name for name in names), "Spaces in names not allowed"
Returns true if exactly one of items in seq passes pred. Cheks for boolean true if pred is omitted.
Finds first item in seq passing pred or first that is true if pred is omitted.
Returns its argument.
Returns function accepting any args, but always returning x.
Returns function calling its argument with passed arguments.
A re-export of functools.partial(). Can be used in a variety of ways. DSLs is one of them:
field = dict
json_field = partial(field, json=True)
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.
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.
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.
Returns composition of functions:
extract_int = compose(int, r'\d+')
Supports Extended predicate/mapper semantics.
is_private = re_tester('^_')
is_public = complement(is_private)
is_even_int = all_fn(isa(int), even)
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?')
)
Transforms a flat wrapper into a decorator with arguments or not. @decorator passes special call object as first argument to a wrapper.
An easy way to create decorators. 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
phone = Phone.objects.get(number=call.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 # ...
You can easily create decorators with arguments too:
@decorator
def joining(call, sep):
return sep.join(imap(sep.__class__, call()))
Usage example shown in joining() docs.
You can see more examples in flow and debug submodules source code.
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:
choices = {1: 'a', 2: ' b', 4: ' c '}
get_caption = compose(silent(string.strip), choices)
map(get_caption, [0, 1, 2, 3, 4])
# -> [None, 'a', 'b', None, 'c']
Note
avoid silencing non-primitive functions, use ignore() instead and even then be careful not to swallow exceptions unintentionally.
Same as silent(), but able to specify errors to catch and default to return in case of error caught. errors can either be exception class or tuple of them.
Constructs function that raises given exception with given arguments on any invocation.
Every call of decorated function retried up to tries times if any subclass of errors occurs (could be exception class or a tuple of them).
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. If errors is not supplied then fall back is done for any Exception:
fallback(
(partial(send_mail, admin_mail, message), SMTPException),
partial(log.error, message),
raiser(FeedbackError, "Unable to log error")
)
If function fails to complete fails times in a row, calls to it will be intercepted for timeout with exception 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
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.
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().
Returns a function that calls re_find() for it’s sole argument. It’s main purpose is quickly constructing mapper functions for map() and friends.
See also Extended predicate/mapper semantics.
Returns a function that calls re_test() for it’s sole argument. Aimed at quick construction of predicates for use in filter() and friends.
See also Extended predicate/mapper semantics.
Joins sequence by sep. Same as sep.join(seq), but forcefully converts all elements to separator type, str by default.
See also joining().
Cuts prefix from given string if it’s present.
Cuts suffix from given string if it’s present.
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 return some_value on fail instead of None.
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 raise LookupError 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)
Same as make_lookuper(), but returns None on memory miss.
Same as memoize(), but doesn’t use cached results older than timeout. It can be either number of seconds or datetime.timedelta. Also, doesn’t support skipping.
Returns function checking if it’s argument is of any of given types.
Split labels from ids:
labels, ids = split_by(isa(str), values)
These functions check if value is Mapping, Sequence, list, tuple or iterator respectively.
Checks if value is a list or a tuple, which are both sequences and collections.
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)
Tests if value is iterable.
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))
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
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 to False 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(...):
# ...
print_calls() always prints everything.
Will log or print all function errors providing function arguments causing them.
Can be combined with silent() or ignore() to trace occasionally misbehaving function:
@silent
@log_errors(logging.warning)
def guess_user_id(username):
initial = first_guess(username)
# ...
Checks if x is None. Handy with filtering functions:
remove(isnone, list_of_dirty_data)
Plays nice with silent(), which returns None on fail:
remove(isnone, imap(silent(int), strings_with_numbers))
Note that it’s usually simpler to use keep() or compact() if you don’t need to distinguish between None and other falsy values.
Checks if x is not None. A shortcut for complement(isnone) meant to be used when bool is not specific enough. Compare:
select_values(notnone, data_dict) # removes None values
compact(data_dict) # removes all falsy values
Increments its argument by 1.
Decrements its argument by 1.
Checks if x is even.
Checks if x is odd.
You can also look at the code or create an issue.