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Dictionary comprehension syntax
Use curly braces for the resulting dictionary and a colon between the key expression and the value expression:
{key_expression: value_expression for item in iterable}The for clause supplies successive values from an iterable. On each iteration, Python evaluates the key and value expressions and adds the pair to the new dictionary. For example:
squares = {number: number ** 2 for number in range(5)}
# {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}The left expression is the key; the right expression is its associated value. That distinguishes a dictionary comprehension from a list comprehension, which produces a list rather than key-value pairs. The Python language reference documents the syntax and evaluation rules.
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How to filter entries with an if clause
Put an optional if clause after the iterable. If its condition is false, that iteration contributes no entry:
even_squares = {
number: number ** 2
for number in range(10)
if number % 2 == 0
}
# {0: 0, 2: 4, 4: 16, 6: 36, 8: 64}Read this as: iterate over the numbers, keep only even ones, then use each retained number as a key and its square as the value. The filter applies to the entire key-value pair, not just one side of the colon.
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Transforming an existing dictionary
Use .items() to iterate over each existing key and value when you want to preserve keys while changing values:
prices_usd = {"notebook": 4.00, "pen": 1.50}
prices_eur = {
item: price * 0.85
for item, price in prices_usd.items()
}This example uses 0.85 as an illustrative multiplier from an OpenStax exercise, not as a current exchange rate. See OpenStax, Introduction to Python Programming, section 10.5 for instructional examples and exercises.
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Using multiple for and if clauses
You can include additional for and if clauses. Python processes them from left to right, nesting them in that order. Consider this comprehension:
products = {
(row, column): row * column
for row in range(2)
for column in range(3)
}It visits every column for each row, equivalent to nested loops:
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products = {}
for row in range(2):
for column in range(3):
products[(row, column)] = row * columnWhen a comprehension has several clauses, translating it mentally into ordinary loops is a reliable way to check its order and behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What happens when keys repeat?
A dictionary can contain only one value for a given key. If a later iteration computes a key that is already present, its value replaces the earlier value:
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pairs = [("a", 1), ("b", 2), ("a", 3)]
result = {key: value for key, value in pairs}
# {"a": 3, "b": 2}That replacement is normal dictionary behavior, as described in the Python tutorial’s discussion of dictionaries. If you need to keep every value associated with a repeated key, collect values into lists or choose a data structure designed for multiple values per key.
Scope and evaluation order
A comprehension’s loop target does not overwrite a same-named variable in the surrounding scope. Comprehensions run in an implicitly nested scope, except that the leftmost iterable is evaluated in the surrounding scope.
The Python 3.15.0rc3 language reference also states: “At runtime, when a dictionary comprehension is evaluated, the expressions are evaluated from left to right.” In particular, Python evaluates the key before the value in a dictionary comprehension from Python 3.8 onward. Before 3.8, that key-versus-value order was not well-defined; CPython had evaluated the value first. Most comprehensions with no side effects do not depend on this distinction.
When to use a comprehension instead of a loop
A comprehension is a good fit when the mapping can be expressed as one clear key/value calculation, optionally with a straightforward filter. If the operation needs multiple statements, complicated branching, or intermediate steps, an explicit loop is usually easier to read and maintain.
PEP 274, which proposed the syntax, describes dict comprehensions as a more succinct idiom than a traditional loop and illustrates creating dictionaries from key-value expressions. It also discusses an approach using a list of pairs passed to dict(); its comments about intermediate lists are historical rationale, not a current performance benchmark.
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