5 cool things you can do with itertools

Posted by Bob on Thu 12 January 2017 in Modules • 2 min read

What is itertools?

Itertools is a stdlib module that provides functions that create iterators "inspired by constructs from APL, Haskell, and SML". See the docs page, pymotw, as well as this great EuroPython preso.

5 cool things you can do with itertools

I created this notebook so you can follow along. Below a summary:

1. Use product to get all combinations between two iterators

Common game techniques: build a card deck or roll two dices:

import itertools
import random

# from Fluent Python
ranks = [str(n) for n in range(2, 11)] + list('JQKA')
suits = 'S D C H'.split() # spades diamonds clubs hearts
# but instead of double list comprehension, using product
cards = ['{}{}'.format(*p) for p in itertools.product(suits, ranks)]

Another use case: roll 2 dices (SO is your friend):

dice = range(2, 7)
random.choice([p for p in itertools.product(dice, repeat=2)])

# output: 
(5, 2)
(2, 5)
(2, 5)
(6, 5)

2. Show a progress spinner for a console app

From before-mentioned EuroPython preso:

import itertools
import sys
import time

def spinner(seconds):
    """Show an animated spinner while we sleep."""
    symbols = itertools.cycle('-\|/')
    tend = time.time() + seconds
    while time.time() < tend:
        # '\r' is carriage return: return cursor to the start of the line.
        sys.stdout.write('\rPlease wait... ' + next(symbols)) # no newline
    print() # newline

if __name__ == "__main__":

3. Use dropwhile to get counts of >= n in a Counter dict

Given a books Counter object, get me books with >= 2 occurences.

I needed this for my tools of titans kata to see which books got recommended more than once by Tim Ferriss' podcast guests.

def get_multiple_mentions(books, keep=2):
    for key, count in itertools.dropwhile(lambda key_count: key_count[1] >= keep, books.most_common()):
        del books[key]
    return books

# filters all books with count (occurence = 1) out:
Counter({'4-hour-workweek-escape-live-anywhere': 2,
        'alchemist-paulo-coelho': 2,
        'atlas-shrugged-ayn-rand': 3,
        'black-swan-improbable-robustness-fragility': 2,
        'checklist-manifesto-how-things-right': 2,

4. Combinations and permutations

For the difference read this great explanation.

Given a list of friends how many pairs can be formed?

friends = 'bob tim julian fred'.split()
# as these are "Combinatoric generators" I consume them here for example purposes using list()
list(itertools.combinations(friends, 2))

# output:
[('bob', 'tim'),
('bob', 'julian'),
('bob', 'fred'),
('tim', 'julian'),
('tim', 'fred'),
('julian', 'fred')]

How many 4 letter strings can you from 7 letters? (hint: upcoming challenge)

import string
letters = random.sample(string.ascii_uppercase, 7)
len(list(itertools.permutations(letters, 4)))
# = 840 (7 * 6 * 5 * 4)

5. Groupby to count amount of keys for specific value in dict

Count the number of keys for a value, for example count the number of users (keys) that have email as pref (value) in a user_prefs dict.

This example is based on the one I found at pymotw.

# set up dict
users = 'tim bob julian sue fred frank maria'.split()
prefs = 'email phone IM email F2F email phone'.split()
user_prefs = dict(zip(users, prefs))

user_prefs_sorted = sorted(user_prefs.items(), key=itemgetter(1))
for pref, users in itertools.groupby(user_prefs_sorted, key=itemgetter(1)):
    print(pref, list(map(itemgetter(0), users)))

# output: 
F2F ['fred']
IM ['julian']
email ['frank', 'tim', 'sue']
phone ['bob', 'maria']

Other uses?

Where do you use itertools mostly for? Share your experience in the comments below ...

>>> next(PyBites)

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