Use a list comprehension as the default when you want a list of transformed or selected values. Use map() when applying an existing function is clearer, filter() when a named predicate makes selection easy to understand, and a generator expression or iterator-returning built-in when you want lazy iteration rather than an immediate list. Choose a regular loop if a compact expression would be difficult to read. There is no universally fastest form; benchmark the actual workload when speed matters.
How the three approaches differ
A list comprehension evaluates its expression and builds a list immediately. In Python 3, map() and filter() return iterators; they produce values as they are consumed. A generator expression is another lazy option. The Python Functional Programming HOWTO describes map() and filter() as built-ins that duplicate features of generator expressions: Python Functional Programming HOWTO.
| Choice | Result | Good fit | Clarity consideration |
|---|---|---|---|
| List comprehension | Builds a list immediately | Straightforward transformation, filtering, or both | Dense or nested expressions can be difficult to scan |
map() or filter() |
Returns an iterator in Python 3 | Applying an existing function or predicate when that form reads cleanly | Lambdas and chained calls may obscure a simple operation |
| Generator expression | Returns a lazy generator | Streaming values or avoiding a list allocation until values are consumed | Make lazy evaluation and one-pass consumption clear |
When should you use a list comprehension?
Use one when the transformation or condition is short and the result should be a list. Comprehensions put the output expression and any selection condition in one place.
Transform every item
names = [user.name for user in users]Select matching items
active_users = [user for user in users if user.is_active]The condition is evaluated for each candidate item; if it is false, the item is skipped. The Python expression reference documents this filtering behavior.
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When is map() the clearer choice?
Use map(function, iterable) when you already have a function and applying it to each item reads naturally. For example, str.strip names the operation directly:
names = list(map(str.strip, raw_names))The explicit list() consumes the iterator and builds the list. If you do not need a list, you can pass the iterator to a consumer that accepts an iterable. map() can also take multiple iterables, passing corresponding values to the mapped function; see the Python Functional Programming HOWTO.
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When is filter() a good fit?
Use filter(predicate, iterable) when a predicate function expresses the selection clearly, especially if it is already named and reusable. If the condition is short and local, a comprehension often makes the test and selected value easier to read together:
active_users = [user for user in users if user.is_active]Like map(), filter() returns an iterator in Python 3. Wrap it in list() only when an immediate list is needed.
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When should you use a generator expression?
Choose a generator expression when values can be consumed one at a time and you want to avoid constructing a list up front:
names = (user.name for user in users)This is useful for streaming or passing values directly to another iterator-consuming operation. Laziness does not guarantee that the whole program avoids storing the values: a consumer may later materialize them, and a generator is typically consumed as it is iterated rather than reused like a list.
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When should you use a regular loop?
Prefer a loop when the work needs multiple statements, side effects, exception handling, or branching that would make a comprehension or a chain of built-ins hard to follow. The goal is clear intent, not the shortest possible syntax.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which form is faster?
Syntax alone does not establish a speed winner. The result can depend on the workload, callable, whether the output must be materialized, and the Python version. Avoid blanket claims that map() or comprehensions are always faster.
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PEP 709, published on 2023-08-01, describes a Python 3.12 implementation change that inlines comprehensions in the cases it covers, removing a separate code object and single-use function object. That change is not a general benchmark comparing every use of comprehensions, map(), and filter().
If performance matters, benchmark representative code on the Python version used by your application. Include list construction in the measurement when the consumer needs a list; otherwise, comparing a lazy iterator with a materialized list may not reflect equivalent work.
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