To count how often each value appears in a Python dictionary, pass its values view to collections.Counter: Counter(my_dict.values()). To count items from any iterable, use Counter(iterable). Both approaches require hashable items.
Count repeated values in a dictionary
A dictionary maps keys to values; it does not automatically track how often a value occurs. Use Counter from the standard library to build a frequency map of the values:
from collections import Counter
inventory = {
"first": "apple",
"second": "banana",
"third": "apple",
"fourth": "orange",
"fifth": "banana",
"sixth": "apple",
}
counts = Counter(inventory.values())
print(counts)
# Counter({'apple': 3, 'banana': 2, 'orange': 1})inventory.values() supplies the observations, so this counts repeated values—not the dictionary’s keys or its total number of entries. Counter is a dictionary subclass: its keys are the distinct values and its values are their counts. See the Python 3.14 Counter documentation.
Count items in a list or another iterable
For a list, tuple, or other iterable of hashable items, pass the iterable directly to Counter:
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from collections import Counter
items = ["apple", "banana", "apple", "orange", "banana", "apple"]
counts = Counter(items)
print(counts["apple"]) # 3Because a Counter uses its items as dictionary keys, each item must be hashable. Strings, numbers, and tuples of hashable values are common examples; mutable lists and dictionaries are not valid counter items.
Use defaultdict when counting needs custom logic
If each item needs additional processing as it is counted, a defaultdict(int) provides a convenient loop. Its integer factory supplies zero when a missing key is first accessed with square brackets:
from collections import defaultdict
counts = defaultdict(int)
for item in items:
counts[item] += 1For a regular dictionary, counts[item] += 1 raises KeyError if item is not already a key. A defaultdict creates and stores a missing entry when accessed through counts[item]; calling counts.get(item) does not trigger its factory. The Python 3.14 defaultdict documentation describes this behavior.
Choose the right counting approach
| Approach | Best for | Missing-key behavior |
|---|---|---|
Counter(iterable) |
A concise tally or common frequency operations | Reading an absent item returns 0. |
defaultdict(int) |
A custom loop that does more than tally each item | Indexed access creates and stores a zero-valued entry. |
Plain dict |
When you deliberately manage key initialization yourself | Reading an absent key with square brackets raises KeyError. |
For most frequency counts, Counter is the clearest choice. Use defaultdict(int) when counting is one part of per-item logic. A plain dictionary also works if you initialize a key before incrementing it, but direct incrementing alone fails for an unseen key.
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Get the most frequent items
Counter.most_common(n) returns up to n items as (item, count) pairs, ordered from highest count to lowest. For ties, items retain their first-encounter order:
counts = Counter(["apple", "banana", "apple", "banana", "orange"])
print(counts.most_common(2))
# [('apple', 2), ('banana', 2)]Here, apple appears before banana in the input, so it appears first in the tied result. See the Python 3.14 most_common documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Handle zero and negative counts
A counter can contain zero or negative values. Assigning zero does not remove an entry; delete the key explicitly if it should no longer be present:
counts["apple"] = 0
# "apple" remains a key in counts
del counts["apple"]
# "apple" is removedThis matters when inspecting the counter’s keys or converting it to another mapping: a zero-count entry remains present until deleted. The Counter documentation covers zero and negative counts.
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