Navigation

Counter

A Counter is a container that keeps track of how many times equivalent values are added. It can be used to implement the same algorithms for which bag or multiset data structures are commonly used in other languages.

Initializing

Counter supports three forms of initialization. Its constructor can be called with a sequence of items, a dictionary containing keys and counts, or using keyword arguments mapping string names to counts.

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})

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 Counts

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

The elements() method returns an iterator that produces all of the items known to the Counter.

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']

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:  235331
i:  201032
a:  199554

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 '\nCombined counts:'
print c1 + c2

print '\nSubtraction:'
print c1 - c2

print '\nIntersection (taking positive minimums):'
print c1 & c2

print '\nUnion (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})

Navigation