filtered =
Built-in Data Structures

Meaning

Creates a new list containing only the elements of an existing iterable that satisfy a condition, using a list comprehension. It returns a new list, leaving the original iterable unchanged. This is ideal for filtering data in a concise, readable way.

Primary Function

Data filtering

Communicative Purpose

Select elements that meet a predicate while constructing a list in a concise, readable way.

Pattern

[expression for item in iterable if condition]

Core Structure

[ ... for ... in ... if ... ]

Função primária

Data filtering

Propósito comunicativo

Select elements that meet a predicate while constructing a list in a concise, readable way.

Situações de gatilho

When you need to extract even numbers from a list; when you want to keep items that match a predicate without mutating the original list.

Contextos

General‑purpose Python scripts, data‑processing pipelines, algorithm implementations.

Padrão

[expression for item in iterable if condition]

Estrutura central

[ ... for ... in ... if ... ]

Slots de substituição

output_expr: expression, loop_var: identifier, iterable: expression, predicate: expression

Colocados típicos

  • if condition
  • for loop_var in iterable
  • list literals

Substituições comuns

  • Using filter() with a lambda
  • using an explicit for‑loop with append()

Erros comuns

Omitting the if clause, forgetting the surrounding brackets (producing a generator expression instead of a list), introducing side‑effects in the expression part.

Similar / contraste

filter() returns an iterator, while a list comprehension creates a list immediately; a generator expression (parentheses) is lazy and memory‑efficient compared to a list comprehension (brackets).

Interferências

Coming from JavaScript: using map() for filtering is incorrect; assuming list comprehensions always produce a list—generator expressions require parentheses.

Família do chunk

  • list comprehension
  • generator expression
  • filter
  • map

Nuance

List comprehensions evaluate eagerly, which can be costly for very large data; prefer a generator expression or itertools.filterfalse for lazy evaluation.

Efeito pragmático

Makes code concise and expressive; eliminates boilerplate loops and manual list appends; reduces risk of forgetting to initialize the list.

Dica de memória

Even numbers list comprehension: filtered = [x for x in my_list if x % 2 == 0]

Nota

List comprehensions are eager and create a new list; for large or infinite data prefer generator expressions or itertools.filterfalse for lazy evaluation.

Upgrade path

Use a generator expression for lazy evaluation: filtered = (x for x in my_list if x % 2 == 0)

Frequência: Very highFormulaicidade: Semi-fixedTipo de construção: list comprehensionPrioridade de aquisição: Automatic productionPrioridade de output: BothTag de espaçamento: Immediate

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