Use a list comprehension to create a new list containing only the items that meet a condition. For example, this keeps even numbers: [number for number in numbers if number % 2 == 0]. The general pattern is [expression for item in iterable if condition]: the condition decides what is included, and the expression decides what value is added to the result.
How do you filter a list in Python?
Put the item variable after for, then add an if condition that evaluates to true for items you want to keep:
numbers = [1, 2, 3, 4, 5, 6]
evens = [number for number in numbers if number % 2 == 0]
print(evens) # [2, 4, 6]The comprehension creates a new list, preserves the input order, and leaves the original list unchanged. This is a clear default when you need a list of matching items. The Python tutorial’s list-comprehension documentation describes this pattern.
How do you select items and transform them at the same time?
Place the output expression before for to change each selected value. Put the condition after if to decide which input items are included:
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words = ["apple", "", "pear"]
upper_words = [word.upper() for word in words if word]
print(upper_words) # ['APPLE', 'PEAR']Here, word.upper() creates each output value, while if word excludes empty strings. The Python expression reference covers the comprehension syntax.
Be careful with truthiness conditions: if item excludes every falsey value, including 0, False, '', and None. If you only mean to remove None, write that explicitly:
values = [0, 1, None, 2]
not_none = [value for value in values if value is not None]
print(not_none) # [0, 1, 2]A conditional expression inside the output expression performs a different job: it chooses the output value for each included item. For example, ["positive" if n > 0 else "not positive" for n in numbers] transforms every number; it does not filter any out.
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How do you keep the index of each selected item?
Use enumerate() when the position matters. It yields an index and item together; by default, the index starts at zero:
items = ["skip", "keep", "keep"]
selected = [(i, item) for i, item in enumerate(items) if item == "keep"]
print(selected) # [(1, 'keep'), (2, 'keep')]See the Python documentation for enumerate() for its optional starting index.
When should you use filter() or a generator expression?
Use filter() when you have a reusable predicate function or want to process results as an iterator instead of immediately building a list. In current Python, filter(predicate, items) returns an iterator. Convert it to a list if a concrete list is required:
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def is_even(number):
return number % 2 == 0
numbers = [1, 2, 3, 4, 5, 6]
evens = list(filter(is_even, numbers))
print(evens) # [2, 4, 6]A generator expression is another iterator-producing option: (item for item in items if condition). Both approaches yield matching values as you iterate; wrapping one in list() consumes it and materializes the results. The Python Functional Programming HOWTO notes that list comprehensions can achieve the same filtering effect.
How do you select items that fail a condition?
Use itertools.filterfalse() when you want the items for which a predicate is false. It returns an iterator:
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from itertools import filterfalse
numbers = [1, 2, 3, 4, 5, 6]
not_even = list(filterfalse(is_even, numbers))
print(not_even) # [1, 3, 5]For a short condition, a comprehension may be easier to read: [number for number in numbers if number % 2 != 0]. The itertools documentation for filterfalse() describes its iterator behavior.
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How do you filter data using a separate selector list?
Use itertools.compress(data, selectors) when each data item has a corresponding selector. Items are yielded when the paired selector is truthy:
from itertools import compress
names = ["Ari", "Bo", "Cam"]
keep = [True, False, True]
selected = list(compress(names, keep))
print(selected) # ['Ari', 'Cam']The itertools documentation for compress() explains this parallel-iterator pattern.
How do you select records by a field?
Test the field directly in the comprehension. For a list of dictionaries:
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records = [
{"name": "Ari", "status": "active"},
{"name": "Bo", "status": "inactive"},
]
active_records = [record for record in records if record["status"] == "active"]For tuples where status is the second value, use record[1] == "active" instead. operator.itemgetter() retrieves fields and can act as a reusable key function for operations that accept one; it does not itself filter records. See the operator documentation for itemgetter().
What if you only need the first match?
Do not build a list of every match if you only need one. Use next() with a generator expression to get the first match, or provide a default if there might be no match:
numbers = [1, 3, 4, 6]
first_even = next((number for number in numbers if number % 2 == 0), None)
print(first_even) # 4The default here is None; choose a default that makes sense for your data. Without a default, next() raises StopIteration if the iterator has no remaining item.
Which selection method should you choose?
| Need | Use | Result |
|---|---|---|
| A new list with a short condition | List comprehension | List |
| A reusable predicate or iterator processing | filter() |
Iterator; use list() if you need a list |
| Iterator processing with an inline condition | Generator expression | Iterator; use list() if you need a list |
| Items that do not satisfy a predicate | itertools.filterfalse() |
Iterator |
| Data paired with truthy selector values | itertools.compress() |
Iterator |
| Matching items with their positions | enumerate() in a comprehension |
List of index-item pairs |
Avoid removing elements from a list while looping over that same list: changes to its length and positions can cause items to be skipped. Building a filtered result separately keeps the selection rule explicit.
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