Intermediate Python Programming Course

Introduction

This tutorial is designed to elevate your Python programming skills by covering intermediate concepts and techniques. You'll revisit basic constructs like lists, strings, and dictionaries with a focus on lesser-known features, and explore advanced topics such as threading, multiprocessing, and context managers. By the end of this guide, you'll have a solid understanding of intermediate Python programming that you can apply in real-world scenarios.

Step 1: Review of Lists

• Understand basic operations with lists, including:
  • Adding items using append() and extend()
  • Removing items with remove() and pop()
  • Slicing lists for sublists
• Explore advanced list comprehensions for concise code:

  squares = [x**2 for x in range(10)]
  

Step 2: Working with Tuples

• Recognize the immutability of tuples and their use cases.
• Create tuples and access elements:

  my_tuple = (1, 2, 3)
  print(my_tuple[0])  # Outputs: 1
  

Step 3: Understanding Dictionaries

• Learn about key-value pairs and dictionary methods:
  • Adding and updating entries with update()
  • Accessing values with keys
  • Iterating through keys and values
• Use dictionary comprehensions for efficient data management:

  squares_dict = {x: x**2 for x in range(5)}
  

Step 4: Exploring Sets

• Understand the properties of sets, including uniqueness of elements.
• Perform set operations such as union, intersection, and difference:

  set_a = {1, 2, 3}
  set_b = {2, 3, 4}
  print(set_a & set_b)  # Outputs: {2, 3}
  

Step 5: String Manipulation

• Review string methods like join(), split(), and string formatting.
• Utilize f-strings for cleaner formatting in Python 3.6+:

  name = "World"
  greeting = f"Hello, {name}!"
  

Step 6: Working with Collections

• Explore specialized collection types from the collections module:
  • Counter for counting hashable objects
  • defaultdict for handling missing keys gracefully
  • namedtuple for creating tuple subclasses with named fields

Step 7: Utilizing Itertools

• Learn about the itertools module for efficient looping:
  • Use count(), cycle(), and repeat() for infinite iterators.
  • Combine iterables with chain() and produce combinations with combinations().

Step 8: Implementing Lambda Functions

• Understand anonymous functions and their syntax:

  add = lambda x, y: x + y
  print(add(2, 3))  # Outputs: 5
  

Step 9: Handling Exceptions and Errors

• Use try, except, and finally blocks for robust error handling.
• Create custom exceptions by extending the Exception class.

Step 10: Logging in Python

• Implement logging to track events and errors:

  import logging
  logging.basicConfig(level=logging.INFO)
  logging.info("This is an info message")
  

Step 11: Working with JSON

• Parse JSON data using the json module:

  import json
  data = json.loads('{"name": "John", "age": 30}')
  

Step 12: Generating Random Numbers

• Use the random module:

  import random
  print(random.randint(1, 10))  # Outputs a random integer between 1 and 10
  

Step 13: Understanding Decorators

• Learn how decorators modify function behavior:

  def decorator_function(original_function):
      def wrapper_function():
          print("Wrapper executed before {}".format(original_function.__name__))
          return original_function()
      return wrapper_function
  
  @decorator_function
  def display():
      print("Display function executed")
  

Step 14: Threading vs Multiprocessing

• Understand the difference between threading (concurrent execution) and multiprocessing (parallel execution).
• Use the threading module to create threads and the multiprocessing module for process-based parallelism.

Step 15: Implementing Context Managers

• Use context managers to manage resources:

  with open('file.txt', 'r') as file:
      contents = file.read()
  

Conclusion

In this tutorial, you have learned various intermediate Python concepts, from data structures to advanced programming techniques. To further enhance your skills, practice these concepts by working on real projects or contributing to open-source. Continue your Python journey by exploring additional resources, and don't hesitate to refer back to this guide as you apply these techniques in your coding endeavors.