Python Basics — filter() Function

The filter() Function

filter() selects items from an iterable that satisfy a condition. It returns an iterator containing only the items for which the function returns True.

Syntax

filter(function, iterable)

• function — a function that returns True or False for each item
• iterable — the data to filter

Basic Usage

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Keep only even numbers
evens = list(filter(lambda x: x % 2 == 0, numbers))
print(evens)  # [2, 4, 6, 8, 10]

Filtering with Named Functions

def is_positive(n):
    return n > 0

values = [-5, -2, 0, 3, 7, -1, 8]
positive = list(filter(is_positive, values))
print(positive)  # [3, 7, 8]

Filtering with Lambda

# Words longer than 4 characters
words = ["hi", "hello", "hey", "greetings", "yo", "welcome"]
long_words = list(filter(lambda w: len(w) > 4, words))
print(long_words)  # ['hello', 'greetings', 'welcome']

# Ages of adults (18+)
ages = [12, 18, 25, 8, 30, 15, 22]
adults = list(filter(lambda age: age >= 18, ages))
print(adults)  # [18, 25, 30, 22]

Removing Falsy Values — filter(None, iterable)

When you pass None as the function, filter removes all falsy values:

data = [0, 1, "", "hello", None, True, False, [], [1, 2], {}, {"a": 1}]

truthy = list(filter(None, data))
print(truthy)  # [1, 'hello', True, [1, 2], {'a': 1}]

Falsy values in Python: 0, "", None, False, [], (), {}, set().

Practical Use: Cleaning Data

# Remove empty strings from user input
raw_inputs = ["Alice", "", "Bob", "  ", "", "Charlie"]
non_empty = list(filter(lambda s: s.strip(), raw_inputs))
print(non_empty)  # ['Alice', 'Bob', '  ', 'Charlie']

# Using None — removes only empty strings, not whitespace-only
non_empty = list(filter(None, raw_inputs))
print(non_empty)  # ['Alice', 'Bob', '  ', 'Charlie']

filter() vs List Comprehension

Feature	filter()	List Comprehension
Syntax	filter(func, iterable)	[x for x in iterable if cond]
Returns	Iterator (lazy)	List (eager)
Transform + filter	Needs map() too	Both in one expression
Readability	Good with existing functions	Better for most cases

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# filter()
evens = list(filter(lambda x: x % 2 == 0, numbers))

# List comprehension — more Pythonic
evens = [x for x in numbers if x % 2 == 0]

print(evens)  # [2, 4, 6, 8, 10]

# Combined filter + transform: squares of even numbers
# filter + map
result1 = list(map(lambda x: x**2, filter(lambda x: x % 2 == 0, numbers)))

# List comprehension — much cleaner
result2 = [x**2 for x in numbers if x % 2 == 0]

print(result1)  # [4, 16, 36, 64, 100]
print(result2)  # [4, 16, 36, 64, 100]

filterfalse() from itertools

The opposite of filter() — keeps items where the function returns False:

from itertools import filterfalse

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Keep odd numbers (where is_even returns False)
odds = list(filterfalse(lambda x: x % 2 == 0, numbers))
print(odds)  # [1, 3, 5, 7, 9]

Splitting Data with filter + filterfalse

from itertools import filterfalse

def is_passing(score):
    return score >= 60

scores = [45, 72, 88, 55, 90, 38, 65]

passed = list(filter(is_passing, scores))
failed = list(filterfalse(is_passing, scores))

print(f"Passed: {passed}")   # Passed: [72, 88, 90, 65]
print(f"Failed: {failed}")   # Failed: [45, 55, 38]

Practical Examples

Filtering Dictionaries

products = [
    {"name": "Laptop", "price": 75000, "in_stock": True},
    {"name": "Mouse", "price": 500, "in_stock": False},
    {"name": "Keyboard", "price": 2000, "in_stock": True},
    {"name": "Monitor", "price": 25000, "in_stock": True},
]

# In-stock products under 30000
affordable = list(filter(
    lambda p: p["in_stock"] and p["price"] < 30000,
    products
))
for p in affordable:
    print(f"{p['name']}: ₹{p['price']}")
# Keyboard: ₹2000
# Monitor: ₹25000

Filtering with Multiple Conditions

students = [
    {"name": "Alice", "gpa": 3.8, "year": 3},
    {"name": "Bob", "gpa": 2.5, "year": 4},
    {"name": "Charlie", "gpa": 3.9, "year": 2},
    {"name": "Diana", "gpa": 3.5, "year": 4},
]

# Seniors with GPA > 3.0
honor_roll = list(filter(
    lambda s: s["year"] == 4 and s["gpa"] > 3.0,
    students
))
print([s["name"] for s in honor_roll])  # ['Diana']

Summary

• filter(func, iterable) keeps items where func returns True
• Returns an iterator — wrap in list() to see results
• filter(None, iterable) removes falsy values
• filterfalse() from itertools keeps where function returns False
• Prefer list comprehensions for most filtering in Python
• Use filter() when passing an existing predicate function

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