Overview of the Collections Module

The Collections module implements high-performance container datatypes (beyond the built-in types list, dict and tuple) and contains many useful data structures that you can use to store information in memory.

This article will be about the Counter object.

Counter

A Counter is a container that tracks how many times equivalent values are added.

It can be used to implement the same algorithms for which other languages commonly
use bag or multiset data structures.

Importing the module

Import collections makes the stuff in collections available as:
collections.something

import collections

Since we are only going to use the Counter, we can simply do this:

from collections import Counter

Initializing

Counter supports three forms of initialization.

Its constructor can be called with a sequence of items (iterable), a dictionary containing keys and counts (mapping, or using keyword arguments mapping string names to counts (keyword args).

import collections

print collections.Counter(['a', 'b', 'c', 'a', 'b', 'b'])

print collections.Counter({'a':2, 'b':3, 'c':1})

print collections.Counter(a=2, b=3, c=1)

The results of all three forms of initialization are the same.

$ python collections_counter_init.py

Counter({'b': 3, 'a': 2, 'c': 1})
Counter({'b': 3, 'a': 2, 'c': 1})
Counter({'b': 3, 'a': 2, 'c': 1})

Create and Update Counters

An empty Counter can be constructed with no arguments and populated via the update() method.

import collections

c = collections.Counter()
print 'Initial :', c

c.update('abcdaab')
print 'Sequence:', c

c.update({'a':1, 'd':5})
print 'Dict :', c

The count values are increased based on the new data, rather than replaced.

In this example, the count for a goes from 3 to 4.

$ python collections_counter_update.py

Initial : Counter()

Sequence: Counter({'a': 3, 'b': 2, 'c': 1, 'd': 1})

Dict    : Counter({'d': 6, 'a': 4, 'b': 2, 'c': 1})

Accessing Counters

Once a Counter is populated, its values can be retrieved using the dictionary API.

import collections

c = collections.Counter('abcdaab')

for letter in 'abcde':
    print '%s : %d' % (letter, c[letter])

Counter does not raise KeyError for unknown items.

If a value has not been seen in the input (as with e in this example), its count is 0.

$ python collections_counter_get_values.py

a : 3
b : 2
c : 1
d : 1
e : 0

Elements

The elements() method returns an iterator over elements repeating each as many times as its count.

Elements are returned in arbitrary order.

import collections

c = collections.Counter('extremely')

c['z'] = 0

print c

print list(c.elements())

The order of elements is not guaranteed, and items with counts less than zero are not included.

$ python collections_counter_elements.py

Counter({'e': 3, 'm': 1, 'l': 1, 'r': 1, 't': 1, 'y': 1, 'x': 1, 'z': 0})
['e', 'e', 'e', 'm', 'l', 'r', 't', 'y', 'x']

Most_Common

Use most_common() to produce a sequence of the n most frequently encountered
input values and their respective counts.

import collections

c = collections.Counter()
with open('/usr/share/dict/words', 'rt') as f:
    for line in f:
        c.update(line.rstrip().lower())

print 'Most common:'
for letter, count in c.most_common(3):
    print '%s: %7d' % (letter, count)

This example counts the letters appearing in all of the words in the system dictionary to produce a frequency distribution, then prints the three most common letters.

Leaving out the argument to most_common() produces a list of all the items, in order of frequency.

$ python collections_counter_most_common.py

Most common:
e: 234803
i: 200613
a: 198938

Arithmetic

Counter instances support arithmetic and set operations for aggregating results.

import collections

c1 = collections.Counter(['a', 'b', 'c', 'a', 'b', 'b'])
c2 = collections.Counter('alphabet')

print 'C1:', c1
print 'C2:', c2

print '
Combined counts:'
print c1 + c2

print '
Subtraction:'
print c1 - c2

print '
Intersection (taking positive minimums):'
print c1 & c2

print '
Union (taking maximums):'
print c1 | c2

Each time a new Counter is produced through an operation, any items with zero or negative counts are discarded.

The count for a is the same in c1 and c2, so subtraction leaves it at zero.

$ python collections_counter_arithmetic.py

C1: Counter({'b': 3, 'a': 2, 'c': 1})
C2: Counter({'a': 2, 'b': 1, 'e': 1, 'h': 1, 'l': 1, 'p': 1, 't': 1})

#Combined counts:
Counter({'a': 4, 'b': 4, 'c': 1, 'e': 1, 'h': 1, 'l': 1, 'p': 1, 't': 1})

#Subtraction:
Counter({'b': 2, 'c': 1})

#Intersection (taking positive minimums):
Counter({'a': 2, 'b': 1})

#Union (taking maximums):
Counter({'b': 3, 'a': 2, 'c': 1, 'e': 1, 'h': 1, 'l': 1, 'p': 1, 't': 1})

Counting words

Tally occurrences of words in a list.

cnt = Counter()

for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
    cnt[word] += 1

print cnt

Counter({'blue': 3, 'red': 2, 'green': 1})

The counter takes an iterable and could also be written like this:

mywords = ['red', 'blue', 'red', 'green', 'blue', 'blue']

cnt = Counter(mywords)

print cnt

Counter({'blue': 3, 'red': 2, 'green': 1})

Find the most common words

Find the ten most common words in Hamlet

import re

words = re.findall('w+', open('hamlet.txt').read().lower())

print Counter(words).most_common(10)

[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631), ('you', 554),  ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]

Sources

Please don’t forget to read the links below for more information.

http://www.doughellmann.com/PyMOTW/collections/
http://docs.python.org/2/library/collections.html#collections.Counter

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