Loop Through a Dictionary in Python

Start with the most direct pattern: iterate key–value pairs using dictionary view objects in the documentation. Views let you traverse keys, values, or items without copying, and they automatically reflect changes to the underlying dictionary.

Method 1: Iterate key–value pairs with `dict.items()`

Create or load a dictionary to process.

users = {"alice": 3, "bob": 7, "carol": 1}

Loop over key–value tuples with items().

for name, score in users.items():
    print(name, score)

Replace the print with your logic (compute, transform, or call an API) while both the key and value are available.

Method 2: Iterate values only with `dict.values()`

Use the values view when you don’t need keys.

scores = {"a": 10, "b": 20, "c": 30}

for value in scores.values():
    print(value)

Perform computations that only depend on values (for example, totals or thresholds).

Method 3: Iterate keys (default) and access values by key

Loop over the dictionary directly to get keys.

inventory = {"apples": 5, "oranges": 2, "bananas": 8}

for item in inventory:
    print(item, inventory[item])

Prefer this when you mostly need keys and only occasionally read a value.

Method 4: Iterate in a specific order with `sorted()`

Sort by key for predictable traversal.

prices = {"pear": 1.4, "apple": 0.9, "orange": 1.2}

for k in sorted(prices):
    print(k, prices[k])

Sort by value using a key function.

for k, v in sorted(prices.items(), key=lambda item: item[1]):
    print(k, v)

Reverse the order by passing reverse=True to the reference.

for k in sorted(prices, reverse=True):
    print(k, prices[k])

Method 5: Safely modify a dictionary while iterating

Iterate over a static snapshot when deleting or adding items.

# Remove even values safely
data = {"x": 2, "y": 3, "z": 4}

for k, v in list(data.items()):  # snapshot
    if v % 2 == 0:
        del data[k]

print(data)  # {'y': 3}

Build new entries in a temporary dict or list, then merge after the loop to avoid size-change errors.

updates = {}
for k, v in list(data.items()):
    if v < 5:
        updates[f"{k}_small"] = v * 10

data.update(updates)

Tip: Iterating a live view while changing size raises a runtime error. A snapshot (list(d.items()) or list(d)) avoids this and keeps the mutation logic clear.

Method 6: Destructively consume items with `popitem()` (LIFO)

Use a while loop to remove and process items until the dictionary is empty.

work = {"t1": "A", "t2": "B", "t3": "C"}

while work:
    key, value = work.popitem()
    # Process and discard
    print("done:", key, value)

Choose this pattern when you explicitly want to drain the dictionary and free memory as you go.

Method 7: Transform or filter with a dictionary comprehension

Build a transformed dictionary in one pass.

prices = {"apple": 1.00, "orange": 0.80, "banana": 0.50}
discounted = {k: round(v * 0.9, 2) for k, v in prices.items()}
print(discounted)  # {'apple': 0.9, 'orange': 0.72, 'banana': 0.45}

Filter items by condition while iterating.

cheap = {k: v for k, v in prices.items() if v < 0.9}
print(cheap)  # {'orange': 0.8, 'banana': 0.5}

Method 8: Traverse multiple dictionaries as one with `collections.ChainMap`

Combine multiple mappings into a single, dictionary-like view.

from collections import ChainMap

defaults = {"color": "blue", "lang": "en"}
overrides = {"lang": "fr"}

cfg = ChainMap(overrides, defaults)
for k, v in cfg.items():
    print(k, v)  # lang fr  |  color blue

Read keys across the chain while updates apply to the first mapping; see the documentation for behavior and methods.

Choose the right loop for the job

Use items() to process keys and values together.
Use values() for value-only computations.
Use direct key iteration for key-focused logic.
Use sorted() for predictable order and reversed order when needed.
Use a snapshot or popitem() to avoid size-change errors while mutating.

A few small patterns cover nearly every need: items() for paired access, sorted() for order, and snapshots or popitem() for safe mutation. Keep loops focused on one goal, and your dictionary code will stay fast and easy to read.

Method 1: Iterate key–value pairs with dict.items()

Method 2: Iterate values only with dict.values()

Method 3: Iterate keys (default) and access values by key

Method 4: Iterate in a specific order with sorted()