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A fancy and practical functional tools

Project Description

A collection of fancy functional tools focused on practicality.

Inspired by clojure, underscore and my own abstractions. Keep reading to get an overview or read the docs. Or jump directly to cheatsheet.

Works with Python 2.6+, 3.3+ and pypy.

Installation

pip install funcy

Overview

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

And much more.

Dive in

Funcy is an embodiment of ideas I explain in several essays:

Running tests

To run the tests using your default python:

pip install -r test_requirements.txt
py.test

To fully run tox you need all the supported pythons to be installed. These are 2.6+, 3.3+, PyPy and PyPy3. You can run it for particular environment even in absense of all of the above:

tox -e py27
tox -e py34
tox -e lint
Release History

Release History

This version
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1.8

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0.2.0

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0.1.0

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