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Functional programming in Python with generators and other utilities.


  • 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 3.6+


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(

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]),

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.

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