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 or lconcat instead.
join(colls)
Joins collections of same type into one. Same as merge, but accepts iterable of collections.
Use cat and lcat for non-type preserving sequence join.
All functions in this section support extended_fns.
walk(f, coll)
Returns a collection of same type as coll consisting of its elements mapped with the 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 use map when there is no need to preserve collection type.
walk_keys(f, coll)
Walks keys of coll, mapping them with the 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 translationImportant to note that it preserves collection type whenever this is simple py3:dict, ~py3:collections.defaultdict, ~py3:collections.OrderedDict or any other mapping class or a collection of pairs.
walk_values(f, coll)
Walks values of coll, mapping them with the 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=...) <partial> instead of py3:sorted to sort in non-default way.
Note that walk_values() has special handling for defaultdicts <py3:collections.defaultdict>. It constructs new one with values mapped the same as for ordinary dict, but a default factory of new defaultdict would be a composition of f 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 by pred constructing a 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_keys(pred, coll)
Select part of a dict or a collection of pairs with keys passing the 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 the given predicate:
# Leave only str values
select_values(isa(str), values)
# Construct a dict of methods
select_values(inspect.isfunction, cls.__dict__)compact(coll)
Removes falsy values from given collection. When compacting a dict all keys with falsy values are removed.
Extract integer data from request:
compact(walk_values(silent(int), request_dict))merge_with(f, *dicts) join_with(f, dicts, strict=False)
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}Historically join_with() will return a dict as is if there is only one, which might be inconvenient. To always apply the summarization func use strict param:
join_with(list, [{1: 2}]) # {1: 2}
join_with(list, [{1: 2}], strict=True) # {1: [2]}
join_with(len, [{1: 2}], strict=True) # {1: 1}zipdict(keys, vals)
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(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 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))omit(mapping, keys)
Returns a copy of mapping with keys omitted. Preserves collection type:
omit({'a': 1, 'b': 2, 'c': 3}, 'ac')
# -> {'b': 2}zip_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:
error = sum((x - y) ** 2 for x, y in zip_values(result, reference))zip_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 zip_dicts(items, old_items)
if abs(new - old) >= PRECISION]
lines = {id: cnt * price for id, (cnt, price) in zip_dicts(amounts, prices)}See also zip_values.
get_in(coll, path, default=None)
Returns a value corresponding to path in nested collection:
get_in({"a": {"b": 42}}, ["a", "b"]) # -> 42
get_in({"a": {"b": 42}}, ["c"], "foo") # -> "foo"Note that missing key or index, i.e. KeyError and IndexError result into default being return, while trying to use non-int index for a list will result into TypeError. This way funcy stays strict on types.
set_in(coll, path, value)
Creates a nested collection with the value set at specified path. 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 nested collection with a value at specified path updated:
update_in({"a": {}}, ["a", "cnt"], inc, default=0)
# -> {"a": {"cnt": 1}}del_in(coll, path)
Creates a nested collection with path removed:
del_in({"a": [1, 2, 3]}, ["a", 1])
# -> {"a": [1, 3]}Returns the collection as is if the path is missing.
has_path(coll, path)
Checks if path exists in the given nested collection:
has_path({"a": {"b": 42}}, ["a", "b"]) # -> True
has_path({"a": {"b": 42}}, ["c"]) # -> False
has_path({"a": [1, 2]}, ["a", 0]) # -> Truewhere(mappings, cond) lwhere(mappings,cond)
Looks through each value in given sequence of dicts and returns an iterator or a list of all the dicts that contain all key-value pairs in cond:
lwhere(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(where(plays, author="Shakespeare"))
# => {"title": "The Two Gentlemen of Verona", ...}pluck(key, mappings) lpluck(key, mappings)
Returns an iterator or a list of values for key in each mapping in the given sequence. Essentially a shortcut for:
map(operator.itemgetter(key), mappings)pluck_attr(attr, objects) lpluck_attr(attr, objects)
Returns an iterator or a list of values for attr in each object in the given sequence. Essentially a shortcut for:
map(operator.attrgetter(attr), objects)Useful when dealing with collections of ORM objects:
users = User.query.all()
ids = lpluck_attr('id', users)invoke(objects, name, args,kwargs) linvoke(objects, name,args, **kwargs)
Calls named method with given arguments for each object in objects and returns an iterator or a list of results.
is_distinct(coll, key=identity)
Checks if all elements in the 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)all([pred], seq)
Checks if pred holds for every element in a seq. If pred is omitted checks if all elements of seq are truthy -- same as in built-in py3: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)any([pred], seq)
Returns True if pred holds for any item in given sequence. If pred is omitted checks if any element of seq is truthy.
Check if there is a needle in haystack, using extended predicate semantics <extended_fns>:
any(r'needle', haystack_strings)none([pred], seq)
Checks if none of items in given sequence pass pred or is truthy 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"
# Or same using extended predicate semantics
assert none(' ', names), "..."one([pred], seq)
Returns true if exactly one of items in seq passes pred. Cheks for truthiness if pred is omitted.
some([pred], seq)
Finds first item in seq passing pred or first that is true if pred is omitted.
empty(coll)
Returns an empty collection of the same type as coll.
iteritems(coll)
Returns an iterator of items of a coll. This means key, 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]