Python Lesson 26: Advanced Python

Congratulations on reaching the final lesson! Here are the advanced Python concepts that will make you a stronger developer: list comprehensions, generators, decorators, and context managers.

List Comprehensions

# Old way (loop)
squares = []
for n in range(10):
    squares.append(n ** 2)

# List comprehension (Pythonic)
squares = [n ** 2 for n in range(10)]
print(squares)  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# With condition
evens = [n for n in range(20) if n % 2 == 0]
print(evens)    # [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

# Transform strings
names = ["alice", "bob", "charlie"]
upper = [name.upper() for name in names]
print(upper)  # ['ALICE', 'BOB', 'CHARLIE']

# Dict comprehension
scores = {"Alice":95, "Bob":87, "Charlie":92}
grades = {k: "A" if v>=90 else "B" for k, v in scores.items()}
print(grades)

Generators — Memory Efficient Loops

# Normal function returns all values at once (uses memory)
def make_squares(n):
    return [x**2 for x in range(n)]

# Generator yields one at a time (saves memory!)
def gen_squares(n):
    for x in range(n):
        yield x ** 2   # yields one value, then pauses

# Use a generator
for sq in gen_squares(5):
    print(sq)  # 0 1 4 9 16

# Generator expression (like list comprehension)
gen = (x**2 for x in range(1000000))  # no memory used yet!
print(next(gen))  # 0 — gets next value on demand
print(next(gen))  # 1

Decorators

import time

# A decorator wraps a function with extra behavior
def timer(func):
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)  # call original
        end = time.time()
        print(f"{func.__name__} took {end-start:.4f}s")
        return result
    return wrapper

# Apply with @ syntax
@timer
def slow_function():
    time.sleep(0.1)
    return "done"

result = slow_function()
# slow_function took 0.1001s

Context Managers

# You already know one: with open(...)
with open("file.txt") as f:
    data = f.read()
# File closes automatically!

# Create your own context manager
from contextlib import contextmanager

@contextmanager
def timer_ctx(label):
    import time
    start = time.time()
    yield             # code inside with block runs here
    end = time.time()
    print(f"{label}: {end-start:.4f}s")

with timer_ctx("My task"):
    # do something that takes time
    sum(range(1000000))
# My task: 0.0245s

What to Learn Next