Python Advanced Course Demo: Unlock Your Python Superpowers!
Why Advanced Python?
* **Efficiency:** Write faster code, use less memory.
* **Readability:** Craft elegant, maintainable, "Pythonic" solutions.
* **Robustness:** Build applications that handle errors gracefully and manage resources effectively.
* **Problem Solving:** Tackle complex challenges with powerful tools.
* **Career Growth:** Essential for Data Science, Web Dev, Automation, AI/ML, and more!
Your Journey: The Advanced Python Course
- High-Level Modules:
- Module 1: Foundation of Advanced Python (Decorators, Generators, Context Managers)
- Module 2: Advanced OOP Concepts (Inheritance, Metaclasses, Descriptors)
- Module 3: Multithreading & Multiprocessing (Concurrency, Parallelism)
- Module 4: Data Manipulation & Visualization (NumPy, Pandas, Matplotlib)
- Module 5: Databases (SQLite, SQL Fundamentals)
- Module 6: Web Development (Building APIs and web applications using either using Flask, Django, or FastAPI)
- Module 7: Async Programming (High-performance non-blocking I/O)
- Module 8: Web-Based GUI Frameworks (Streamlit, Gradio)
Module 1: Foundation of Advanced Python
-
Key Concepts:
-
Decorators: Modify or extend function behavior before or after execution without altering its core code.. Example: @login_required def view_profile(): return user_data
-
Generators: Memory-Efficient Iteration. Functions that yield values one at a time, creating iterators that produce data on demand, saving memory for large datasets. Example: def read_large_file(): for line in file: yield line
-
Context Managers: Ensure proper resource management (with statement). Example: with open('data.txt', 'r') as f: ensures the file is always closed.
-
Comprehensions: Concise creation of data structures (lists, dicts, sets). Example: squares = [x*x for x in range(5) if x % 2 == 0] (list comprehension).
-
itertools Module: Powerful tools for efficient iteration. Example: count() - Counts infinitely from a starting number: from itertools import count
for i in count(1): # Counts from 1 print(i) if i == 5: # Stop at 5 break
Output: 1, 2, 3, 4, 5
-
Real-World Use Case:
- Web Frameworks: Decorators for Web routing (@app.route in Flask), authentication checks, logging, caching in Flask/Django.
- Data Processing: Generators for processing large log files or CSVs without loading all into memory, optimizing resource use.
Decorators: Enhancing Functions Without Changing Them
* **What:** A function that takes another function as an argument, adds some functionality, and returns a new function.
* **Syntax:** The elegant `@decorator_name` above a function.
* **Why:** Cleanly separate concerns, add cross-cutting features (logging, timing, auth).
Generators - Efficient Iteration for Large Data
* **What:** Functions that `yield` values one at a time, instead of returning a whole list.
* **How:** They "pause" execution and resume when the next value is requested.
* **Why:** Extremely memory efficient, especially for large or infinite sequences.
- Simple Example:
def number_generator(start, end):
current = start
while current <= end:
yield current
current += 1
numbers = number_generator(1, 5)
for num in numbers:
print(num)
numbers = number_generator(1, 5)
print(next(numbers))
print(next(numbers))
- Real-World Use Case:
- "You are processing a log file that's several gigabytes. Instead of loading the entire file into memory (which would crash your program), a generator processes it line by line, using minimal memory." They help manage memory by processing items one at a time rather than loading everything at once.
Module 2: Advanced OOP Concepts (1/3)
-
-
Inheritance & Polymorphism:
-
Inheritance: Reusing code by creating new classes based on existing ones.
- Example: A
Car class inheriting features from a Vehicle class.
-
Polymorphism: Using a single interface (method) that behaves differently for various object types.
- Example: A
drive() method working for both Car and Truck objects.
-
Use Case: Building extensible frameworks, handling diverse data types uniformly.
Module 2: Advanced OOP Concepts (2/3)
* **Special Methods (Dunder Methods):**
* Methods like `__init__`, `__str__`, `__add__` that let your objects act like built-in types.
* **Simple Example:**
```python
class Point:
def __init__(self, x, y):
self.x, self.y = x, y
def __add__(self, other):
return Point(self.x + other.x, self.y + other.y)
def __str__(self):
return f"({self.x}, {self.y})"
p1 = Point(1, 2)
p2 = Point(3, 4)
print(p1 + p2) # Outputs: (4, 6)
```
Module 2: Advanced OOP Concepts (3/3)
-
- Metaclasses:
- A metaclass is simply a class that creates other classes. It controls how new classes are made and what features they have.
- Example: Enforcing that every class you define must have a specific method or attribute.
- Use Case: ORMs (e.g., Django's models), enforcing API contracts, domain-specific languages.
-
Descriptors:
- Mini-objects that manage how an attribute (like
my_object.name) is accessed, set, or deleted.
- Example: The
@property decorator uses descriptors. A descriptor could validate that my_object.age is always a positive number.
- Use Case: Implementing
property, classmethod, staticmethod, data validation.
-
Example:
class AgeValidator:
def __get__(self, obj, owner):
return obj._age
def __set__(self, obj, value):
if value < 0:
raise ValueError("Age cannot be negative")
obj._age = value
class Person:
age = AgeValidator() # Using the descriptor
def __init__(self, name, age):
self.name = name
self.age = age # This will use the descriptor
# Usage:
person = Person("John", 25)
print(person.age) # Output: 25
# This will raise an error
try:
person.age = -5
except ValueError as e:
print(e) # Output: Age cannot be negative
```
Module 3: Multithreading and Multiprocessing (1/2)
Concurrency & Parallelism for Performance
-
Concurrency vs. Parallelism: Understanding the difference and when to use each.
- Concurrency: Managing multiple tasks that appear to run simultaneously (e.g., waiting for network requests).
- Parallelism: Truly simultaneous execution of multiple tasks (e.g., heavy CPU computations).
-
Multithreading: Ideal for I/O-bound tasks (network requests, file operations) where GIL has less impact.
```python
# Concurrent example using threading
import threading
def task1(): print("Starting task 1") # Simulating I/O operation time.sleep(2) print("Task 1 done")
def task2(): print("Starting task 2") # Simulating I/O operation time.sleep(2) print("Task 2 done")
Running tasks concurrently
thread1 = threading.Thread(target=task1) thread2 = threading.Thread(target=task2) thread1.start() thread2.start() ```
Module 3: Multithreading and Multiprocessing (2/2)
-
Multiprocessing: Best for CPU-bound tasks, bypassing the GIL by running tasks in separate processes.
```python
Parallel example using multiprocessing
from multiprocessing import Process
CPU-intensive task
def calculate_square(numbers): for n in numbers: print(f"Square of {n} is {n*n}")
Running in parallel
if name == 'main': numbers1 = [1,2,3] numbers2 = [4,5,6] p1 = Process(target=calculate_square, args=(numbers1,)) p2 = Process(target=calculate_square, args=(numbers2,)) p1.start() p2.start() ```
-
When to Use:
Threads: For waiting tasks (reading files, API calls)
Processes: For CPU heavy tasks (calculations, data processing)
Module 4: Data Manipulation and Visualization
- NumPy: Foundation for numerical computing with powerful N-dimensional array objects.
- Use Case: Efficient mathematical operations, linear algebra, array broadcasting.
- Pandas: High-performance, easy-to-use data structures (
Series, DataFrame) for data analysis.
- Use Case: Data cleaning, transformation, aggregation, time-series analysis.
- Example (Pandas DataFrame):
import pandas as pd
data = {'City': ['NY', 'LA', 'CHI'], 'Population': [8.4, 3.9, 2.7]}
df = pd.DataFrame(data)
print(df)
- Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations.
- Use Case: Generating plots, charts, and graphs for data exploration and reporting.
Module 5: Databases (SQLite)
Storing and Managing Persistent Data
- SQL Fundamentals: Learn the language to interact with relational databases (
SELECT, INSERT, UPDATE, DELETE).
- Python
sqlite3 Module: Built-in library for connecting to and managing SQLite databases.
- Use Case: Local data storage for desktop applications, small web apps, caching.
- Example (Connecting & Creating Table):
import sqlite3
conn = sqlite3.connect('example.db')
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS users (
id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
email TEXT UNIQUE
)
''')
conn.commit()
conn.close()
- Database Design Principles: Understand normalization, primary/foreign keys for efficient and reliable data structures.
- Real-world Application: User management systems, inventory tracking, content management.
Module 6: Web Development (Django/Flask/FastAPI)
**Building Dynamic Web Applications and APIs**
- Web Frameworks Overview: Introduction to Flask, FastAPI, and Django – their philosophies and use cases.
- Flask: Micro-framework, highly flexible, great for small to medium projects and APIs.
- FastAPI: Modern, high-performance, async-ready, built on Pydantic for data validation.
- Routing & Views: Defining URL endpoints and the functions that handle requests.
- Models & ORM (Django): Object-Relational Mappers to interact with databases using Python objects.
- RESTful API Principles: Designing clean, stateless, and resource-oriented APIs.
Module 7: Async Programming
**Non-Blocking I/O for High Concurrency**
-
What is Async Programming?
- Like a chef cooking multiple dishes at once instead of one at a time
- Lets your program do other tasks while waiting (like waiting for web requests)
-
Example: ```python import asyncio
async def make_coffee(): print("Starting coffee...") await asyncio.sleep(2) # Pretend brewing coffee print("Coffee ready!")
async def make_toast(): print("Toasting bread...") await asyncio.sleep(1) # Pretend toasting print("Toast ready!")
async def breakfast(): # Make both at the same time! await asyncio.gather(make_coffee(), make_toast())
Run it
asyncio.run(breakfast()) ```
-
Key Points:
- async: Tells Python this function can pause and resume
- await: Like saying "pause here and wait"
- Perfect for web apps, file operations, and API calls
-
asyncio Library: Python's standard library for writing concurrent code using the async/await syntax.
- Use Case: Building high-performance web servers, network clients, real-time applications.
- Asynchronous I/O Operations: Integrating with libraries like
aiohttp for async web requests.
-
Benefits: Improved responsiveness and scalability for I/O-bound applications.
Module 8: Web-Based GUI Frameworks (Streamlit & Gradio)
**Rapid Prototyping & Interactive Applications (Streamlit & Gradio)**
- Introduction to Web GUIs: Building interactive web applications with minimal front-end code.
- Context: Bridging the gap between Python scripts and shareable, interactive tools.
- Streamlit: Turn data scripts into shareable web apps with simple Python.
- Gradio: Quickly create customizable UI components for your ML models.
- Use Case: Demonstrating ML model predictions, creating quick demos for research.
- Key Advantages: Focus on Python logic, automatic UI generation, easy deployment.
Course Projects
- Interactive Data Explorer & Dashboard (Streamlit/Gradio): Visualize and analyze datasets.
- Flask Task Manager with User Authentication: Build a full-stack web application.
- SQLite Command-Line Inventory System: Develop a robust data management tool.
- Pandas & Matplotlib Sales Data Analyzer: Perform in-depth data analysis and reporting.
- FastAPI To-Do List API: Create a high-performance backend service.
- Django Blog with User Authentication: Develop a content management system.
Thank You!
