At this stage, you’ll dive into more powerful features of Python. These concepts make your code faster, smarter, and more professional—the tools that real-world developers use every day.
1. Decorators
Decorators let you add extra functionality to functions without changing their code.
def shout(func):
def wrapper():
print("🔥 Before the function runs")
func()
print("✅ After the function runs")
return wrapper
@shout
def say_hello():
print("Hello, world!")
say_hello()
2. Generators
Generators are memory-efficient ways to create iterators.
def count_up_to(n):
i = 1
while i <= n:
yield i
i += 1
for number in count_up_to(5):
print(number) # 1 2 3 4 5
3. Advanced OOP (Inheritance, Polymorphism) in Python
OOP lets you build more complex systems with relationships between classes.
class Animal:
def speak(self):
print("Some sound")
class Dog(Animal):
def speak(self):
print("Woof!")
class Cat(Animal):
def speak(self):
print("Meow!")
pets = [Dog(), Cat()]
for pet in pets:
pet.speak() # Woof! Meow!
4. Lambda Functions & Functional Programming
Lambda = short, anonymous functions.
numbers = [1, 2, 3, 4, 5]
squares = list(map(lambda x: x**2, numbers))
print(squares) # [1, 4, 9, 16, 25]
5. Iterators & Context Managers in Python
Iterators allow looping through custom objects. Context managers handle resources (like files) safely.
# Custom iterator
class Counter:
def __init__(self, low, high):
self.current = low
self.high = high
def __iter__(self):
return self
def __next__(self):
if self.current > self.high:
raise StopIteration
self.current += 1
return self.current - 1
for num in Counter(1, 3):
print(num) # 1, 2, 3
# Context manager (with)
with open("data.txt", "w") as file:
file.write("Hello Python!")
6. Asynchronous Programming (async/await) in Python
Asynchronous code runs tasks concurrently, making programs faster.
import asyncio
async def greet(name):
print(f"Hello {name}")
await asyncio.sleep(1)
print(f"Goodbye {name}")
async def main():
await asyncio.gather(greet("Alice"), greet("Bob"))
asyncio.run(main())
7. Working with APIs (requests, JSON)
You can fetch and process data from the internet using APIs.
import requests
response = requests.get("https://jsonplaceholder.typicode.com/todos/1")
data = response.json()
print(data["title"])
8. Data Analysis with Pandas & NumPy
These libraries help with handling large datasets.
import pandas as pd
import numpy as np
data = {
"Name": ["Alice", "Bob", "Charlie"],
"Age": [25, 30, 35]
}
df = pd.DataFrame(data)
print(df)
arr = np.array([1, 2, 3, 4, 5])
print(arr.mean()) # Average → 3.0
9. Web Development with Flask/Django
Python can also power websites.
from flask import Flask
app = Flask(__name__)
@app.route("/")
def home():
return "Hello from Flask!"
if __name__ == "__main__":
app.run()
👉 Advanced Level gives you the superpowers of Python: decorators, async code, APIs, data analysis, and even web apps. At this stage, you can build real-world projects like dashboards, chat apps, and machine learning models.