Advanced Python Topics in Week 3: Deep Dive into Python

Week 3: Advanced Python Topics (Days 15–21)

In Week 3, we focus on advancing your Python skills with topics such as advanced object-oriented programming (OOP), testing, Python’s standard libraries, and more. This week will help you become proficient in writing complex and optimized Python applications.

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Day 15: Advanced Object-Oriented Programming (OOP) Concepts

On Day 15, we delve deeper into OOP in Python. You’ll explore advanced concepts like polymorphism, abstract classes, and multiple inheritance.

• What to learn:
• Polymorphism: How different classes can use the same method name.
• Abstract Classes: Creating base classes with abstract methods.
• Multiple Inheritance: Inheriting from more than one class.

  from abc import ABC, abstractmethod

  class Animal(ABC):
      @abstractmethod
      def speak(self):
          pass

  class Dog(Animal):
      def speak(self):
          return "Woof"

  class Cat(Animal):
      def speak(self):
          return "Meow"

  dog = Dog()
  cat = Cat()
  print(dog.speak())  # Output: Woof
  print(cat.speak())  # Output: Meow

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Day 16: Python’s Standard Library (Part 1)

Day 16 introduces Python’s powerful standard library, starting with useful modules such as datetime, math, and random.

• What to learn:
• datetime: Work with dates and times.
• math: Use mathematical functions.
• random: Generate random numbers and perform random actions.

  import datetime
  today = datetime.date.today()
  print(today)  # Output: Current date

  import math
  print(math.sqrt(16))  # Output: 4.0

  import random
  print(random.randint(1, 10))  # Output: Random integer between 1 and 10

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Day 17: Python’s Standard Library (Part 2)

Today, we continue exploring more of Python’s standard library, with an emphasis on modules for working with files and input/output.

• What to learn:
• os: Interact with the operating system.
• sys: Access system-specific parameters.
• pickle: Serialize and deserialize objects.

  import os
  print(os.getcwd())  # Output: Current working directory

  import sys
  print(sys.version)  # Output: Python version

  import pickle
  data = {"name": "Alice", "age": 30}
  with open("data.pickle", "wb") as f:
      pickle.dump(data, f)

  with open("data.pickle", "rb") as f:
      loaded_data = pickle.load(f)
      print(loaded_data)  # Output: {'name': 'Alice', 'age': 30}

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Day 18: Writing Unit Tests in Python

Day 18 focuses on testing your code. Learn to write unit tests using Python’s unittest framework to ensure your code is correct and reliable.

• What to learn:
• How to write unit tests for individual functions.
• Understand assertions like assertEqual(), assertTrue(), and assertRaises().
• Run tests using the unittest module.

  import unittest

  def add(a, b):
      return a + b

  class TestMathOperations(unittest.TestCase):
      def test_add(self):
          self.assertEqual(add(2, 3), 5)

  if __name__ == "__main__":
      unittest.main()

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Day 19: Dependency Management and Virtual Environments

On Day 19, you will learn about dependency management and how to use virtual environments to isolate your projects.

• What to learn:
• pip: Python’s package installer for adding external libraries.
• Create virtual environments using venv to manage dependencies.

  python -m venv myenv
  source myenv/bin/activate  # On Windows, use myenv\Scripts\activate
  pip install requests

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Day 20: Reflection and Dynamic Typing in Python

Day 20 introduces reflection in Python, allowing you to inspect and modify classes and objects dynamically.

• What to learn:
• getattr(): Retrieve an attribute of an object.
• setattr(): Set the value of an attribute.
• type(): Get the type of an object.

  class Person:
      def __init__(self, name):
          self.name = name

  p = Person("Alice")
  print(getattr(p, 'name'))  # Output: Alice
  setattr(p, 'name', 'Bob')
  print(p.name)  # Output: Bob

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Day 21: Memory Management and Optimization

On Day 21, we focus on optimizing Python applications. Learn how to handle memory efficiently and understand how Python’s garbage collection works.

• What to learn:
• Understand Python’s memory management and garbage collection.
• Use the gc module to manage garbage collection.
• Profile your Python code using the time and cProfile modules to optimize performance.

  import gc
  gc.collect()  # Forces garbage collection

  import time
  start_time = time.time()
  # Code to profile
  end_time = time.time()
  print(f"Execution time: {end_time - start_time} seconds")

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Conclusion of Week 3

By the end of Week 3, you will have gained advanced knowledge of Python, including object-oriented programming, testing, optimization, and using Python’s powerful standard libraries. This knowledge will be invaluable as you build more complex and efficient applications.

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Next Steps

In Week 4, you will apply everything you’ve learned by working on a final project. You’ll have the opportunity to build a real-world application and further enhance your Python skills. Stay tuned for Week 4!