Meaning
It builds a new list containing only the elements of an existing iterable that satisfy a given boolean expression. This provides a concise, readable alternative to writing an explicit for‑loop with conditional appends, reducing boilerplate and potential errors. Use it whenever you need to extract a subset of data based on a predicate.
Primary Function
Data filtering
Communicative Purpose
Selects elements that meet a predicate, producing a filtered list.
Pattern
[item for item in iterable if condition]
Core Structure
[... for ... in ... if ...]
Função primária
Data filtering
Propósito comunicativo
Selects elements that meet a predicate, producing a filtered list.
Situações de gatilho
Data analysis: extracting only positive numbers from a numeric list; Web scraping: discarding URLs that do not match a required pattern; Machine learning preprocessing: removing entries with missing or invalid feature values
Contextos
Common in data processing scripts, scientific computing, web backends, and any situation where a list needs to be filtered.
Padrão
[item for item in iterable if condition]
Estrutura central
[... for ... in ... if ...]
Slots de substituição
item: any type, iterable: iterable of items, condition: boolean expression involving item.
Colocados típicos
- Often combined with built‑ins like sum()
- len()
- or passed to other functions
- can be nested for multi‑dimensional filtering.
Substituições comuns
- filter(lambda item: condition
- iterable) or an explicit for‑loop with append().
Erros comuns
Assuming the comprehension modifies the original list; using side‑effects in the condition; forgetting that the result is a list (in Python 3).
Similar / contraste
map() for transformation, filter() for lazy filtering, generator expression (item for item in iterable if condition) for lazy evaluation.
Interferências
Coming from languages like Java: may expect filter to mutate the source; in Python a list comprehension always creates a new list.
Família do chunk
- list comprehension
- generator expression
- map
- filter
Nuance
For large iterables consider a generator expression to avoid memory overhead; the condition should be pure (no side effects).
Efeito pragmático
Makes the filtering intent explicit and concise, improving readability.
Dica de memória
Think of ‘keep what passes the test’.
Nota
List comprehensions create a new list; they do not mutate the original iterable. Avoid side‑effects in the condition expression—keep it pure. For large or potentially infinite iterables prefer a generator expression to avoid unnecessary memory allocation.
Upgrade path
(item for item in iterable if condition) # generator expression for lazy evaluation
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