How do you use dictionary comprehension to transform data payloads in Python?

Dictionary comprehension is the clean, Pythonic way to build or transform a dict in one expression. The syntax mirrors list comprehension: {key_expr: value_expr for variable in iterable if condition}. It is commonly used when processing API response payloads, config maps, or any key-value data that needs normalisation or filtering.

# Invert a dictionary (swap keys and values)
codes = {'USD': 1, 'EUR': 2, 'GBP': 3}
inv   = {v: k for k, v in codes.items()}
# {1: 'USD', 2: 'EUR', 3: 'GBP'}

# Filter an API response payload — keep only active users
users = {
    'alice': {'active': True,  'role': 'admin'},
    'bob':   {'active': False, 'role': 'user'},
    'carol': {'active': True,  'role': 'user'},
}
active_users = {name: data for name, data in users.items()
                if data['active']}
# {'alice': {...}, 'carol': {...}}

# Normalise keys from camelCase API payload to snake_case
import re
payload = {'firstName': 'Alice', 'lastName': 'Smith', 'userId': 42}
to_snake = lambda s: re.sub(r'(?!^)(?=[A-Z])', '_', s).lower()
normalised = {to_snake(k): v for k, v in payload.items()}
# {'first_name': 'Alice', 'last_name': 'Smith', 'user_id': 42}

Nesting a comprehension inside another is possible but quickly becomes hard to read. If the transformation logic exceeds one or two conditions, break it into a helper function and call it from the comprehension. Dict comprehension also pairs naturally with zip() when you have two parallel sequences of keys and values:

headers = ['name', 'age', 'city']
values  = ['Alice', 30, 'NYC']
record  = {k: v for k, v in zip(headers, values)}
# {'name': 'Alice', 'age': 30, 'city': 'NYC'}

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