Python filter() Function - Complete Guide with Examples & Real-World Uses

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Learn how to use Python's filter() function with clear syntax, beginner examples, and real-life use cases. Master filtering lists, dictionaries, and more efficiently!

What is the `filter()` Function?

The filter() function is a built-in Python function that allows you to process an iterable (like a list, tuple, etc.) and extract items that meet a specific condition. It “filters out” elements based on whether they satisfy a given criterion.

Syntax

filter(function, iterable)

function: A function that tests if each element of the iterable meets a condition (returns True or False)
iterable: The sequence you want to filter (list, tuple, etc.)

The function returns a filter object (an iterator), which you can convert to a list or other sequence type.

Basic Usage

Example 1: Filtering even numbers

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

def is_even(num):
    return num % 2 == 0

even_numbers = list(filter(is_even, numbers))
print(even_numbers)  # Output: [2, 4, 6, 8, 10]

Example 2: Using lambda function

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

# Using lambda instead of a separate function
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # Output: [2, 4, 6, 8, 10]

Real-Life Examples

1. Filtering Valid Email Addresses

emails = [
    "user@example.com",
    "invalid.email",
    "another.user@domain.org",
    "missing@dotcom",
    "valid@test.co.uk"
]

def is_valid_email(email):
    return '@' in email and '.' in email.split('@')[-1]

valid_emails = list(filter(is_valid_email, emails))
print(valid_emails)
# Output: ['user@example.com', 'another.user@domain.org', 'valid@test.co.uk']

2. Filtering Products Above a Certain Price

products = [
    {"name": "Laptop", "price": 999.99},
    {"name": "Mouse", "price": 19.99},
    {"name": "Keyboard", "price": 49.99},
    {"name": "Monitor", "price": 199.99},
    {"name": "Headphones", "price": 79.99}
]

expensive_products = list(filter(lambda p: p["price"] > 100, products))
print(expensive_products)
# Output: [{'name': 'Laptop', 'price': 999.99}, {'name': 'Monitor', 'price': 199.99}]

3. Filtering Active Users

users = [
    {"username": "alice", "active": True},
    {"username": "bob", "active": False},
    {"username": "charlie", "active": True},
    {"username": "dave", "active": False},
    {"username": "eve", "active": True}
]

active_users = list(filter(lambda user: user["active"], users))
print(active_users)
# Output: [{'username': 'alice', 'active': True}, 
#          {'username': 'charlie', 'active': True}, 
#          {'username': 'eve', 'active': True}]

Key Points to Remember

filter() returns an iterator, so you often need to convert it to a list or other sequence type.
The function you pass to filter() should return True (to keep) or False (to remove) for each element.
filter() is often used with lambda functions for simple conditions.
It’s more memory efficient than list comprehensions for large datasets since it returns an iterator.

Alternative: List Comprehension

Many filtering operations can also be done with list comprehensions:

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

# Equivalent list comprehension
even_numbers = [x for x in numbers if x % 2 == 0]

Choose based on readability and performance needs - filter() can be more memory efficient for very large datasets.