Python 15: Advanced Techniques for Intermediate Users

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Hello reader👋 I Hope that you have a solid understanding of the fundamentals of Python. However, if you are not yet familiar with them, there is no need to worry. Simply take the time to learn the basics. If you would like me to create a blog post on the topic of getting started with Python, please inform me in comments.

If you have already mastered the basics of Python or Are you an intermediate Python developer looking to level up your skills? In this Python 15 tutorial, we will explore 15 advanced Concepts and best practices that will help you become a more proficient Python programmer.

1. Virtual Environments

Virtual environments are a crucial part of Python development. They help you keep your project dependencies isolated and organized. While there are popular options like Conda, we will start with the basics of creating and managing virtual environments using Python’s built-in venv module. You can also use Conda, a powerful environment and package manager. You can find more about Conda on there website[link here]

To create a virtual environment with venv,check out below follow these steps:

1. Open your command prompt or terminal.
2. Navigate to your project directory using the cd command.
3. Run the following command to create a virtual environment:

python -m venv myenv

1. To activate the virtual environment, use the appropriate command for your operating system:

On Windows:

myenv\Scripts\activate

On macOS and Linux:

source myenv/bin/activate

2. Design Patterns

Discover essential design patterns in Python that can enhance your code organization and maintainability. Familiaries yourself with patterns like Singleton, Factory, and Observer. Knowledge of OOP is must for this.

Singleton Pattern: Grocery Store

Imagine a grocery store where you want to ensure that there’s only one cash register. The Singleton pattern is useful in this scenario to guarantee a single instance of the cash register.

class CashRegister:
    _instance = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(CashRegister, cls).__new__(cls)
            cls._instance.balance = 0
        return cls._instance

    def process_payment(self, amount):
        self.balance += amount

# Usage
register1 = CashRegister()
register1.process_payment(100)
register2 = CashRegister()
print(register2.balance)  #Output: 100 (both registers share the same instance)

Factory Pattern: Cricket Game

In a cricket game simulation, different types of players (batsmen and bowlers) need to be created using a factory pattern.

class Player:
    def play(self):
        pass

class Batsman(Player):
    def play(self):
        return "Batsman is batting!"

class Bowler(Player):
    def play(self):
        return "Bowler is bowling!"

class PlayerFactory:
    def create_player(self, player_type):
        if player_type == "batsman":
            return Batsman()
        elif player_type == "bowler":
            return Bowler()
        else:
            return None

# Usage
player_factory = PlayerFactory()
batsman = player_factory.create_player("batsman")
bowler = player_factory.create_player("bowler")
print(batsman.play())  # Output: "Batsman is batting!"
print(bowler.play())   # Output: "Bowler is bowling!"

Observer Pattern: Cartoon “Tom and Jerry”

Consider a scenario where you want to notify different characters in a cartoon series, such as “Tom and Jerry,” when a new episode is available.

class CartoonSeries:
    def __init__(self):
        self._observers = []

    def subscribe(self, observer):
        self._observers.append(observer)

    def notify_new_episode(self, episode_title):
        for observer in self._observers:
            observer.update(f"New episode available: {episode_title}")

class Character:
    def update(self, message):
        pass

class Tom(Character):
    def update(self, message):
        print(f"Tom: {message}")

class Jerry(Character):
    def update(self, message):
        print(f"Jerry: {message}")

# Usage
cartoon_series = CartoonSeries()
tom = Tom()
jerry = Jerry()
cartoon_series.subscribe(tom)
cartoon_series.subscribe(jerry)
cartoon_series.notify_new_episode("The Chase Continues")

3. PEP 8 and Code Formatting

PEP 8, the official Python style guide, is a set of rules that ensures clean and readable code. Adhering to PEP 8 is essential for these reasons:

• Readability: Consistent code is easier to understand.
• Collaboration: Team members benefit from uniform coding standards.
• Debugging: Clean code is easier to debug.
• Maintainability: Well-structured code is easier to update.
• Community Standards: Conforming to PEP 8 supports Pythons community standards.

Black is an automated code formatter that enforces PEP 8 standards without manual effort. Installation of Black:

pip install black

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4. File Handling

Before learning into different file formats, it is important to understand how to read and write text files. Python provides built in functions like open() to interact with files. You have to learn how to open files in various modes (read, write, append) and manipulate their contents using methods like read(), write(), and close().

4.1 Reading and Writing Text Files

Learn how to read and write text files using Python’s open() function. Understand file modes, and manipulate file contents.

4.2 CSV and JSON Files

Learn about how to work with CSV and JSON files. You’ll master reading, writing, and parsing data in these common formats.

4.3 Other File Formats

Extend your file-handling skills to work with XML, Excel spreadsheets, and other data formats, giving you versatility in data manipulation.

4.4 Binary Files

Understand how to handle binary files, such as images. Learn about encoding, decoding, and working with non-textual data.

4.5 Exception Handling

Explore best practices for error handling during file operations, ensuring your code gracefully handles unexpected situations.

4.6 Context Managers

Use context managers with the with statement to simplify file handling and automatically manage file resources.

5. Advanced Data Structures

Data structures are the building blocks of any programming language, and Python provides a rich set of data structures that go beyond the basics. Lets dive deeper into some of the most commonly used data structures, namely sets, dictionaries, and lists, and learn their advanced features and use cases.

Lists:

List Comprehensions:

List comprehensions allow you to create lists in a concise and efficient manner. Consider a scenario where you have a list of numbers, and you want to create a new list containing the squares of those numbers:

numbers = [1, 2, 3, 4, 5]
squares = [x**2 for x in numbers]

Slicing and Indexing:

Advanced slicing and indexing can be incredibly useful when dealing with large datasets. Suppose you have a list of customer records and want to extract a subset of records for analysis:

customer_data = [
    {"name": "Alice", "age": 32},
    {"name": "Bob", "age": 45},
    {"name": "Charlie", "age": 28},
    # ... many more records
]

young_customers = customer_data[2:5]

Sets:

Set Operations:

Sets are excellent for performing set operations. Lets say you have two sets representing the preferences of users for two different products, and you want to find out which products have been liked by both sets of users:

user1_likes = {"productA", "productB", "productC"}
user2_likes = {"productB", "productC", "productD"}

common_likes = user1_likes.intersection(user2_likes)

Set Comprehensions:

Set comprehensions are valuable when you want to create a set of unique items based on an iterable. Consider a situation where you have a list of tags associated with articles, and you want to compile a set of unique tags:

article_tags = ["programming", "python", "data", "programming", "machine learning"]
unique_tags = {tag for tag in article_tags}

Dictionaries:

Dictionary Comprehensions:

Dictionary comprehensions are handy for creating dictionaries efficiently. Suppose you have a list of products and their prices, and you want to generate a dictionary with discounted prices:

products = ["apple", "banana", "orange"]
original_prices = [1.0, 0.5, 1.2]

discounted_prices = {product: price * 0.9 for product, price in zip(products, original_prices)}

Default Values:

Dictionaries can handle missing keys gracefully. For instance, you can use the get() method to provide default values for missing keys:

fruit_prices = {"apple": 1.0, "banana": 0.5, "orange": 1.2}
price = fruit_prices.get("watermelon", 2.0)  # Returns 2.0 (default value)

Use Cases:

• Lists are versatile and are commonly used for tasks such as data processing, filtering, and data transformation.
• Sets are perfect for maintaining a collection of unique items, and they shine in tasks involving mathematical set operations.
• Dictionaries are widely used for building key-value data structures, making them essential for tasks like data storage, configuration, and caching.

6. Decorators and Generators

Master Python decorators and generators. Decorators can modify the behavior of functions, while generators are excellent for working with large datasets efficiently.

Decorators:

Python decorators are a powerful and flexible feature that allows you to modify or enhance the behavior of functions or methods without changing their core functionality. Decorators are often used for tasks like logging, authentication, and data validation. Here’s a deeper dive into decorators:

Understanding Decorators:

• Decorators are functions that take another function as an argument and return a new function that usually extends or alters the behavior of the original function.
• They are denoted by the @decorator_function syntax placed above the function definition you want to decorate.

Common Use Cases:

• Logging: You can use decorators to log the start and end times of a function call, or the parameters and return values.
• Authentication: You can implement decorators for user authentication, ensuring that only authorized users can access certain functions.
• Caching: Decorators can be used to cache function results, saving time and resources for expensive computations.

Generators:

Generators in Python are a way to create iterable sequences of values or items. They are particularly useful when dealing with large datasets, as they allow you to generate values on-the-fly, rather than storing them in memory. Here’s an overview of generators:

Understanding Generators:

• Generators are created using functions that contain one or more yield statements.
• When a function with a yield statement is called, it returns a generator object, which can be iterated using a for loop or other iteration constructs.
• Generators are memory-efficient because they produce values one at a time and don’t store the entire sequence in memory.

Common Use Cases:

• Large Datasets: Use generators when processing large datasets or files to avoid loading the entire dataset into memory.
• Infinite Sequences: Generators can produce an infinite sequence of values, like an infinite counter or a continuous stream of data.
• Pipelines: Generators are often used to create data processing pipelines, where data is transformed step by step.

7. Error Handling

In Python, you can use try, except, and optionally finally blocks to handle exceptions gracefully.

try: Place the code that might raise an exception in the try block.

except: Specify what should happen if an exception occurs in the except block.

finally (optional): Use the finally block for code that runs whether an exception occurred or not.

class MyCustomError(Exception):
    def __init__(self, message):
        super().__init__(message)

try:
    raise MyCustomError("Custom exception raised.")
except MyCustomError as e:
    print(f"Custom exception caught: {e}")

8. Object-Oriented Programming (OOP)

In Python, Object-Oriented Programming (OOP) is about creating structured code through classes and objects.

• Classes and Objects: Classes define the blueprint, while objects are instances of those blueprints. They encapsulate data and behavior.
• Inheritance and Polymorphism: Inheritance allows you to create new classes based on existing ones, promoting code reusability. Polymorphism simplifies code by enabling uniform interactions with different objects.
• Encapsulation and Abstraction: Encapsulation bundles data and methods, controlling access. Abstraction simplifies complex reality by modeling high-level, generalized classes.
• Practical Application: Apply OOP to real-world projects, enhancing maintainability and extendability.
• Design Patterns: Learn common design patterns like Singleton and Factory for well-established problem-solving.

By mastering OOP in Python, you can write more organized, maintainable, and scalable code for your projects.

9. Regular Expressions

Regular expressions (regex) are powerful pattern-matching tools in Python. They allow you to find and manipulate text based on specific patterns. The re module in Python makes it easy to work with regular expressions.

Basic Regular Expression Patterns

• . (dot): Matches any character except a newline.
• *: Matches 0 or more occurrences of the preceding character.
• +: Matches 1 or more occurrences of the preceding character.
• \d: Matches any digit (0-9).
• \w: Matches any word character (letters, digits, underscores).
• \s: Matches any whitespace character (spaces, tabs, newlines).

Practical Examples

• Email Validation: You can use a regular expression to validate if an input string is a valid email address.
• Phone Number Extraction: Regular expressions can help you extract phone numbers from a large text document by identifying patterns like (123) 456–7890 or 123–456–7890.
• Text Cleanup: They are useful for cleaning up text by removing extra spaces, punctuation, or HTML tags.
• URL Extraction: Extracting URLs from web pages by matching patterns starting with “http://” or “https://”.

10. Version Control

Version control, using tools like Git and GitHub, is a crucial skill for developers. It offers:

• Change Tracking
• Collaboration
• Backup and Recovery
• Branching and Experimentation
• Code Reviews

If you have not already, check out our blog on version control with Mastering GitHub for Game Developers: A Complete Guide. check out below

Mastering GitHub for Game Developers: A Complete Guide with Unity

11. Web & API Development with Flask

Take your Python skills to the web! start with Flask, a micro web framework, and explore the basics of web and API development.

12. Packages and Libraries

Discover essential Python libraries and packages for various domains, such as data science, web development, and more. Learn how to install and use them effectively.

13. Performance Optimization

Optimize your Python code for better performance. Explore techniques like profiling and using libraries like NumPy to speed up your applications.

14. Debugging

Learn debugging best practices and techniques using tools like pdb & print to find and fix issues in your Python code.

15. Use AI

Forget the traditional methods to manually writing everything instead use AI in your daily tasks. Try out AI tools like Github Copilot, ChatGPT, or Code Whisper. They’ll make your work easier and faster!

Also I have additional tip to use JupyterNotebooks for practicing.

Conclusion

Congratulations! You are aware of 15 advanced Python techniques that will take your programming skills to the next level. Keep practicing and applying these tips in your projects to become a Better Python developer.