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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Use a list comprehension when you need a reusable list; use a generator expression when the next operation can consume values one at a time. A generator avoids building the full output collection up front and may stop early, but it is generally a one-pass iterator and is not automatically faster.
What each expression returns
Both forms can apply an expression and optional filters to items from an iterable. Their delimiters signal the result type:
[f(x) for x in items if keep(x)]evaluates the comprehension and returns a list.(f(x) for x in items if keep(x))returns a generator iterator that produces each result as iteration requests it.
If fully consumed, the generator expression yields the same values, in the same order, as the corresponding list comprehension. See the Python language reference on generator expressions.
Choose based on how the result will be used
Use a list comprehension for reuse and list operations
Choose a list when later code needs to index or slice the results, inspect their length directly, iterate over them more than once, or pass them to an interface that expects list operations. The comprehension computes and stores its output before returning. If you start with a generator but later need all those capabilities, materialize it with list(generator); doing so gives up the memory advantage of leaving the results unmaterialized.
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Use a generator expression for incremental consumption
Choose a generator when the consumer can process values as they arrive, especially if the output may be large or the input is an infinite stream. It does not construct the complete output collection up front. If the consumer stops early, values that were never requested are not computed. Python’s functional programming HOWTO describes generator expressions as useful for very large data and infinite streams.
Pass a generator directly to a reduction
When a function can consume an iterable, avoid building a temporary list just to pass its values onward. For example:
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total = sum(x * x for x in values)Because the generator expression is the sole positional argument, the function-call parentheses also group it. If there is another argument or a keyword argument, give the generator its own parentheses:
total = sum((x * x for x in values), start=100)Know when generator code runs
Creating a generator expression is not the same as evaluating every part of it. Python evaluates the iterable expression in the leftmost for clause immediately and creates an iterator from it. The filter clauses, inner iterables, and result expression are evaluated as iteration advances. Consequently, an error in the leftmost iterable can occur at generator creation, while an error in the result expression may not occur until a consumer requests that value. Side effects in those deferred expressions are likewise delayed.
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PEP 289 explains the early evaluation of the outer iterable in terms of normal function-argument evaluation. Guido van Rossum wrote: “I’d be surprised if the one in sum() was raised rather the one in foo(), since the call to foo() is part of the argument to sum(), and I expect arguments to be processed before the function is called.” The quote appears in PEP 289’s “Early Binding versus Late Binding” discussion.
Do not assume one form is faster
A generator avoids holding all output values at once, but that does not guarantee lower runtime. PEP 289’s historical design discussion described performance as roughly comparable for small-to-medium data sets in its context, with generators tending to do better as data grew. That is design rationale, not a current benchmark for every workload or Python implementation.
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PEP 709 reported that its reference implementation made a comprehension-alone microbenchmark up to 2× faster and a comprehension-heavy sample benchmark 11% faster. Those results concern inlined list, set, and dictionary comprehensions in that proposal’s reference implementation; generator expressions were not inlined by the proposal. They are not a direct list-comprehension-versus-generator-expression comparison or a promise about other builds and workloads. See PEP 709.
If speed matters, compare representative code on the Python implementation and version you deploy. Include these factors in your test:
Quick Recap
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- Peak memory use and the amount of output produced.
- Whether the result is consumed once or reused.
- Whether the consumer can stop before reaching the end.
- Measured runtime and memory on representative inputs.
A practical decision rule
- Pick a list comprehension if you need a list’s indexing, slicing, length, or repeat traversal.
- Pick a generator expression if a consumer can use values incrementally, or might stop before consuming them all.
- For a reduction such as
sum, pass a generator directly when no other part of the program needs the intermediate collection. - If the choice is about performance rather than data flow, benchmark both forms in the actual environment.
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