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Functional programming in Python with generators and other utilities.

Project description

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Functional programming in Python with generators and other utilities.

Features

  • Functional-style methods that work with and return generators.
  • Shorthand-style iteratees (callbacks) to easily filter and map data.
  • String object-path support for references nested data structures.
  • 100% test coverage.
  • Python 3.4+

Quickstart

Install using pip:

pip3 install fnc

Import the main module:

import fnc

Start working with data:

users = [{'id': 1, 'name': 'Jack', 'email': 'jack@example.org', 'active': True},
         {'id': 2, 'name': 'Max', 'email': 'max@example.com', 'active': True},
         {'id': 3, 'name': 'Allison', 'email': 'allison@example.org', 'active': False},
         {'id': 4, 'name': 'David', 'email': 'david@example.net', 'active': False}]

Filter active users:

# Uses "matches" shorthand iteratee: dictionary
active_users = fnc.filter({'active': True}, users)
# <filter object at 0x7fa85940ec88>

active_uesrs = list(active_users)
# [{'name': 'Jack', 'email': 'jack@example.org', 'active': True},
#  {'name': 'Max', 'email': 'max@example.com', 'active': True}]

Get a list of email addresses:

# Uses "pathgetter" shorthand iteratee: string
emails = fnc.map('email', users)
# <map object at 0x7fa8577d52e8>

emails = list(emails)
# ['jack@example.org', 'max@example.com', 'allison@example.org', 'david@example.net']

Create a dict of users keyed by 'id':

# Uses "pathgetter" shorthand iteratee: string
users_by_id = fnc.keyby('id', users)
# {1: {'id': 1, 'name': 'Jack', 'email': 'jack@example.org', 'active': True},
#  2: {'id': 2, 'name': 'Max', 'email': 'max@example.com', 'active': True},
#  3: {'id': 3, 'name': 'Allison', 'email': 'allison@example.org', 'active': False},
#  4: {'id': 4, 'name': 'David', 'email': 'david@example.net', 'active': False}}

Select only 'id' and 'email' fields and return as dictionaries:

# Uses "pickgetter" shorthand iteratee: set
user_emails = list(fnc.map({'id', 'email'}, users))
# [{'email': 'jack@example.org', 'id': 1},
#  {'email': 'max@example.com', 'id': 2},
#  {'email': 'allison@example.org', 'id': 3},
#  {'email': 'david@example.net', 'id': 4}]

Select only 'id' and 'email' fields and return as tuples:

# Uses "atgetter" shorthand iteratee: tuple
user_emails = list(fnc.map(('id', 'email'), users))
# [(1, 'jack@example.org'),
#  (2, 'max@example.com'),
#  (3, 'allison@example.org'),
#  (4, 'david@example.net')]

Access nested data structures using object-path notation:

fnc.get('a.b.c[1][0].d', {'a': {'b': {'c': [None, [{'d': 100}]]}}})
# 100

# Same result but using a path list instead of a string.
fnc.get(['a', 'b', 'c', 1, 0, 'd'], {'a': {'b': {'c': [None, [{'d': 100}]]}}})
# 100

Compose multiple functions into a generator pipeline:

from functools import partial

filter_active = partial(fnc.filter, {'active': True})
get_emails = partial(fnc.map, 'email')
get_email_domains = partial(fnc.map, lambda email: email.split('@')[1])

get_active_email_domains = fnc.compose(filter_active,
                                       get_emails,
                                       get_email_domains,
                                       set)

email_domains = get_active_email_domains(users)
# {'example.com', 'example.org'}

Or do the same thing except using a terser “partial” shorthand:

get_active_email_domains = fnc.compose((fnc.filter, {'active': True}),
                                       (fnc.map, 'email'),
                                       (fnc.map, lambda email: email.split('@')[1]),
                                       set)

email_domains = get_active_email_domains(users)
# {'example.com', 'example.org'}

For more details and examples, please see the full documentation at https://fnc.readthedocs.io.

Changelog

v0.4.0 (2019-01-23)

  • Add functions:
    • differenceby
    • duplicatesby
    • intersectionby
    • unionby

v0.3.0 (2018-08-31)

  • compose: Introduce new “partial” shorthand where instead of passing a callable, a tuple can be given which will then be converted to a callable using functools.partial. For example, instead of fnc.compose(partial(fnc.filter, {'active': True}), partial(fnc.map, 'email')), one can do fnc.compose((fnc.filter, {'active': True}), (fnc.map, 'email')).

v0.2.0 (2018-08-24)

  • Add functions:
    • negate
    • over
    • overall
    • overany
  • Rename functions: (breaking change)
    • ismatch -> conforms
    • matches -> conformance
  • Make conforms/conformance (formerly ismatch/matches) accept callable dictionary values that act as predicates against comparison target. (breaking change)

v0.1.1 (2018-08-17)

  • pick: Don’t return None for keys that don’t exist in source object. Instead of fnc.pick(['a'], {}) == {'a': None}, it’s now fnc.pick(['a'], {}) == {}.

v0.1.0 (2018-08-15)

  • First release.

License

The MIT License (MIT)

Copyright (c) 2018, Derrick Gilland

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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