Use collections.Counter to count how often each hashable value appears in a Python dictionary: pass the dictionary’s .values() view to it. To count items in any iterable, pass that iterable directly. If counting is part of a loop with custom per-item logic, use defaultdict(int).
Count repeated values in a dictionary
A dictionary already stores unique keys, so counting occurrences usually means tallying its values. Counter from the standard library’s collections module builds a frequency map:
from collections import Counter
prices = {"apple": 2, "banana": 1, "pear": 2, "plum": 1, "lime": 2}
value_counts = Counter(prices.values())
print(value_counts)
# Counter({2: 3, 1: 2})The result’s keys are the distinct values from the dictionary, and each count is how many times that value occurred. Pass .values() to count values; passing the dictionary itself would count its keys.
Python’s Counter documentation describes it as a dict subclass for counting hashable objects.
Because the items become dictionary keys, they must be hashable.
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Count occurrences in a list or other iterable
The same approach works when the items are in a list, tuple, or another iterable:
from collections import Counter
items = ["apple", "banana", "apple", "orange", "banana", "apple"]
counts = Counter(items)
print(counts)
# Counter({'apple': 3, 'banana': 2, 'orange': 1})This is the concise choice when the job is simply to produce a frequency tally. To retrieve the most frequent items, use most_common():
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print(counts.most_common(2))
# [('apple', 3), ('banana', 2)]most_common(n) returns up to n items in descending count order. When counts tie, items appear in the order they were first encountered.
Use defaultdict(int) for custom counting logic
Choose defaultdict(int) when each item needs additional processing inside the loop, or when you want to build the counts yourself:
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from collections import defaultdict
items = ["apple", "banana", "apple", "orange", "banana", "apple"]
counts = defaultdict(int)
for item in items:
counts[item] += 1
print(dict(counts))
# {'apple': 3, 'banana': 2, 'orange': 1}The int factory returns zero, so the first indexed access to a new item supplies the starting count before it is incremented. Unlike a plain dictionary, this avoids a separate existence check. A plain dictionary expression such as counts[item] += 1 raises KeyError if item is not already a key.
A defaultdict creates and stores a missing entry when it is accessed with square brackets. Its factory is not called by get(); for example, counts.get("kiwi") does not create a "kiwi" entry.
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Choose the counting method
| Approach | Best for | Missing-key behavior |
|---|---|---|
Counter(iterable) |
A direct frequency tally and operations such as most_common() |
Reading a missing key returns 0 |
defaultdict(int) |
A counting loop that also performs custom per-item work | Indexed access creates a missing entry with value 0 |
Plain dict |
When you want to manage initialization explicitly | Reading a missing key with square brackets raises KeyError |
Handle missing, zero, and negative counts
A Counter lookup for an item that has not been counted returns zero without raising KeyError. This lookup does not mean that the item is present in the counter.
counts = Counter(["apple"])
print(counts["pear"]) # 0
print("pear" in counts) # FalseCounter entries may also be set to zero or negative numbers. Setting a count to zero does not remove the entry; delete it explicitly if you want it gone:
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counts["apple"] = 0
del counts["apple"]Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
