Python Counter: Frequency Counting, Most Common Elements, and Arithmetic

Counter is a dict subclass in the collections module. It is designed for one specific job — counting how often things appear — and it does that job with a very clean interface. Where you would normally write a loop, initialise counts, and handle missing keys, Counter does it in one call.

Creating a Counter

from collections import Counter

# From a list
scores = Counter([85, 90, 85, 72, 90, 85, 60])
print(scores)
# Counter({85: 3, 90: 2, 72: 1, 60: 1})

# From a string (counts characters)
letters = Counter("mississippi")
print(letters)
# Counter({'s': 4, 'i': 4, 'p': 2, 'm': 1})

# From keyword arguments
inventory = Counter(apples=5, oranges=3, bananas=8)
print(inventory)
# Counter({'bananas': 8, 'apples': 5, 'oranges': 3})

# Empty counter, then update
votes = Counter()
votes.update(["Alice", "Bob", "Alice", "Alice", "Bob"])
print(votes)
# Counter({'Alice': 3, 'Bob': 2})

most_common() — Top N Elements

from collections import Counter

text = "to be or not to be that is the question"
word_freq = Counter(text.split())

# Top 3 most frequent words
print(word_freq.most_common(3))
# [('to', 2), ('be', 2), ('or', 1)]  — ties broken by order of first appearance

# All elements sorted by frequency (most common first)
print(word_freq.most_common())

most_common(n) uses a partial sort internally — O(k log n) where k is the total number of unique elements. Faster than sorting everything when you only need the top few.

Accessing Counts

from collections import Counter

c = Counter("abracadabra")

# Access like a dict — missing keys return 0, not KeyError
print(c["a"])   # 5
print(c["z"])   # 0  — no error

# Total count of all elements
print(sum(c.values()))   # 11

# Unique elements
print(list(c.keys()))    # ['a', 'b', 'r', 'c', 'd']

# Expand back to a list (each element repeated by count)
print(list(c.elements()))
# ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'r', 'r', 'c', 'd']

Counter Arithmetic

Counter supports +, -, &, and | operations:

from collections import Counter

inventory_a = Counter(apples=5, oranges=3, bananas=2)
inventory_b = Counter(apples=2, oranges=4, grapes=1)

# Addition — combine counts
print(inventory_a + inventory_b)
# Counter({'oranges': 7, 'apples': 7, 'bananas': 2, 'grapes': 1})

# Subtraction — remove counts (drops zero/negative)
print(inventory_a - inventory_b)
# Counter({'apples': 3, 'bananas': 2})  — oranges would be -1, dropped

# Intersection — minimum of each count
print(inventory_a & inventory_b)
# Counter({'oranges': 3, 'apples': 2})  — keys in both, smallest count wins

# Union — maximum of each count
print(inventory_a | inventory_b)
# Counter({'oranges': 4, 'apples': 5, 'bananas': 2, 'grapes': 1})

Practical Patterns

Word frequency analysis:

from collections import Counter
import re

def word_frequency(text):
    """Count word frequency, case-insensitive, ignoring punctuation."""
    words = re.findall(r'\b[a-z]+\b', text.lower())
    return Counter(words)

sample = "Python is great. Python is also fun. Python!"
freq = word_frequency(sample)
print(freq.most_common(3))
# [('python', 3), ('is', 2), ('great', 1)]

Check if one string is an anagram of another:

from collections import Counter

def is_anagram(s1, s2):
    """Return True if s1 and s2 are anagrams (same characters, different order)."""
    return Counter(s1.lower()) == Counter(s2.lower())

print(is_anagram("listen", "silent"))   # True
print(is_anagram("hello", "world"))     # False

Find characters that appear more than once:

from collections import Counter

def find_duplicates(s):
    return [char for char, count in Counter(s).items() if count > 1]

print(find_duplicates("programming"))
# ['r', 'g', 'm']

Counter vs dict.get() vs defaultdict

All three can count, but Counter is the most expressive:

words = ["cat", "dog", "cat", "bird", "dog", "cat"]

# dict.get approach
counts = {}
for w in words:
    counts[w] = counts.get(w, 0) + 1

# defaultdict approach
from collections import defaultdict
counts2 = defaultdict(int)
for w in words:
    counts2[w] += 1

# Counter approach — one line
from collections import Counter
counts3 = Counter(words)

# All three produce the same result, but Counter gives you most_common(),
# elements(), and arithmetic for free

Use Counter when you need counting plus any of its extra features. Use defaultdict(int) when you need a counting dict that you plan to use purely as a regular dict afterward.