🎯 Functional Programming — map, filter, reduce

Process lists of data using functions instead of loops. It's cleaner and often easier to understand!

🎯 map() — Change Every Item

The `map()` function takes a function and applies it to every single item in a list, creating a new list with the transformed values. Instead of writing a loop that goes through each item manually, `map()` does that work for you. This is cleaner, more readable, and often faster than traditional loops.

numbers = [1, 2, 3, 4, 5]

# Double each number
doubled = list(map(lambda x: x * 2, numbers))
print(doubled)  # [2, 4, 6, 8, 10]

💡 Real Example

# Convert all temperatures
celsius = [0, 10, 20, 30]
fahrenheit = list(map(lambda c: (c * 9/5) + 32, celsius))
print(fahrenheit)  # [32.0, 50.0, 68.0, 86.0]

# Extract names from list of people
people = [
    {"name": "Alice", "age": 25},
    {"name": "Bob", "age": 30},
]
names = list(map(lambda p: p["name"], people))
print(names)  # ['Alice', 'Bob']

🔍 filter() — Keep Only What You Need

The `filter()` function runs a test on every item in a list and keeps only the items where the test returns `True`. This is your clean, functional alternative to writing if-statements inside loops. It's especially useful when you need to clean data, remove invalid entries, or extract only certain elements from a collection.

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]

💡 Real Example

# Keep only people over 18
people = [
    {"name": "Alice", "age": 25},
    {"name": "Bob", "age": 17},
    {"name": "Charlie", "age": 30},
]
adults = list(filter(lambda p: p["age"] >= 18, people))
# [{'name': 'Alice', 'age': 25}, {'name': 'Charlie', 'age': 30}]

# Keep only non-empty strings
words = ["python", "", "coding", "", "rocks"]
filtered = list(filter(None, words))  # None filters out empty strings
print(filtered)  # ['python', 'coding', 'rocks']

📈 reduce() — Combine Into One Value

The `reduce()` function takes a list of items and combines them into a single value by repeatedly applying a function. It starts with the first two items, applies your function, then uses that result with the third item, and so on. This is perfect for operations like summing, multiplying, or finding the maximum element in a list.

from functools import reduce

numbers = [1, 2, 3, 4, 5]
total = reduce(lambda a, b: a + b, numbers)
print(total)  # 15 (1+2+3+4+5)

💡 Real Examples

from functools import reduce

# Multiply all numbers
numbers = [2, 3, 4, 5]
product = reduce(lambda a, b: a * b, numbers)
print(product)  # 120

# Find maximum
numbers = [5, 2, 8, 1, 9, 3]
maximum = reduce(lambda a, b: a if a > b else b, numbers)
print(maximum)  # 9

🎨 Combining Them Together

from functools import reduce

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

# Get even numbers, double them, sum them
result = reduce(
    lambda a, b: a + b,
    map(lambda x: x * 2, filter(lambda x: x % 2 == 0, numbers))
)
# filter: [2, 4, 6, 8, 10]
# map: [4, 8, 12, 16, 20]
# reduce: 60
print(result)  # 60

✅ Summary Table

🔑 Key Takeaways

🔗 What's Next?

Now let's learn best practices for writing great functions!

Next: Best Practices →

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