Iteration over mappings

Enables processing each key-value pair in a dictionary without extra lookups.

Data processing: iterating over a dict to transform each entry; Web development: updating configuration values stored in a dict; Analytics: aggregating numeric values from a dict of counters

Common in Python scripts, data processing pipelines, web backends, and any code that works with mapping objects.

for key, value in dict_var.items():

for ... in ... .items():

key: identifier (loop variable for key), value: identifier (loop variable for value), dict_var: identifier (the dictionary to iterate over)

• if statements inside loop
• dict updates
• break/continue
• .get()
• .setdefault()

• for key in my_dict: value = my_dict[key]
• using .items() is preferred
• using .keys() and .values() separately.

Modifying the dictionary while iterating (causing RuntimeError); confusing .items() with .keys() or .values(); using tuple unpacking incorrectly if dict values are not two-element iterables.

for k in my_dict: (iterates only keys); for v in my_dict.values(): (values only); using zip(my_dict.keys(), my_dict.values()) (less efficient).

Coming from languages like C or Java where you need manual index loops; expecting order preservation in older Python versions (pre-3.7) where dict order was arbitrary.

• for loop
• dict comprehension
• items view
• mapping iteration

In Python 3.7+ dict preserves insertion order; iteration reflects that order. .items() returns a view, not a list, so changing the dict size during iteration raises RuntimeError.

Makes intent explicit to process key-value pairs; avoids extra lookups; improves readability.

Think 'key-value loop' when you see for k, v in .items():

.items() returns a view that reflects the dictionary's current state; modifying the dictionary size during iteration raises RuntimeError. In Python 3.7+ dict preserves insertion order, so iteration follows that order.

{k: transform(v) for k, v in my_dict.items()}