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Advanced Python

You can learn advanced Python ideas from the tutorials in this section, which will help you progress toward mastering the language. You can start delving into these lessons that will teach you advanced Python topics and patterns. It takes time to become an expert in Python, but you will eventually become fluent in this lovely programming language. It is worthwhile!

According to surveys conducted by websites like StackOverflow, Python is one of the most popular coding languages in recent years. The versatility of the Python programming language creates a strong demand for developers with different types of experience. For instance, in addition to being a high-level programming language, it is also employed for more generic tasks like constructing web apps and using AI and data visualization.

Python has a vast community that collaborates to create a beautiful programming language because it is open-source and an object-oriented language. Because of how easy it is to use, Python is currently being used by a lot of developers all over the world to create GUI programs, websites, and mobile apps.

There are many free and paid resources available online to learn Python, and in the past, I have also shared many free resources like free books, tutorials, courses, specializations, and certifications. However, many of you have requested more in-depth and advanced Python courses in order to master this crucial programming skill in 2022.

The articles in this Python Advanced series are listed below.

There are numerous ideas for advanced Python features. However, you may learn about many advanced Python concepts in this section of the advanced Python tutorial.

Decorators and Function Annotations

Decorators and function annotations are advanced features of Python that can help in writing more efficient and maintainable code. A decorator is a function that takes another function as an argument and returns a new function, which is a modified version of the original function. Function annotations, on the other hand, allow the programmer to attach metadata to function arguments and return values, specifying their type or other properties.

Decorators can be used for a variety of purposes, such as:

  1. Adding functionality to a function without modifying its source code.
  2. Wrapping a function with additional code, such as error handling or logging.
  3. Implementing memoization or caching to improve performance.
  4. Here is an example of a decorator that adds timing information to a function:


import time

def timer(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"Time taken by {func.__name__}: {end_time - start_time} seconds")
        return result
    return wrapper

def factorial(n):
    if n == 0:
        return 1
        return n * factorial(n-1)


Output: (This output always varies to your CPU’s performance!)

Time taken by factorial: 4.76837158203125e-07 seconds
Time taken by factorial: 7.724761962890625e-05 seconds
Time taken by factorial: 9.965896606445312e-05 seconds
Time taken by factorial: 0.00011610984802246094 seconds
Time taken by factorial: 0.0001327991485595703 seconds
Time taken by factorial: 0.0001499652862548828 seconds
Time taken by factorial: 0.00016617774963378906 seconds
Time taken by factorial: 0.00018358230590820312 seconds
Time taken by factorial: 0.0001995563507080078 seconds
Time taken by factorial: 0.0002167224884033203 seconds
Time taken by factorial: 0.0002357959747314453 seconds

In this example, the timer function is a decorator that takes a function as an argument and returns a new function (wrapper) that adds timing information to the original function. The @timer decorator is applied to the factorial function, which means that whenever factorial is called, the timing information will be printed to the console.

Function annotations allow the programmer to attach metadata to function arguments and return values, specifying their type or other properties. Here is an example of a function with annotations:


def greet(name: str) -> str:
    return f"Hello, {name}!"



Hello, Alice!

In this example, the greet function takes a string argument name and returns a string value. The annotations: str and -> str specify that the name argument and the return value should be of type str. Function annotations can help improve the readability and maintainability of code, especially in larger projects.


The smallest entity or process that an operating system may schedule is a thread. Python has a class called Thread that facilitates multithreaded programming. Due to the fact that multiple threads will now be working on different tasks at once, multithreading is primarily utilized to drastically speed up computation. Python’s threading module must be used to implement threading (since the thread module is deprecated).


import threading

def print_numbers():
    for i in range(1, 6):

def print_letters():
    for letter in 'ABCDE':

if __name__ == '__main__':
    t1 = threading.Thread(target=print_numbers)
    t2 = threading.Thread(target=print_letters)




Output: (For every new execution the output will be different.)





In this example, we create two threads using the threading module. Each thread has a target function (print_numbers and print_letters, respectively) that will be executed concurrently. We then start both threads using the start method, and use the join method to wait for both threads to complete before printing “Done”.

When you run this code, you should see the numbers 1-5 and the letters A – E printed out in an interleaved fashion, demonstrating that the two threads are running concurrently.

Multithreading can be a powerful technique for improving the performance of your Python programs by allowing them to execute multiple tasks simultaneously. However, it’s important to be careful when working with threads to avoid issues like race conditions and deadlocks.

Regular Expressions

Regular expressions in Python, often known as RegEx, are expressions that use particular characters as patterns to be matched. It is employed to determine whether a string or collection of strings has a particular pattern. Along with being quick, it is also very forceful, elegant, and concise. You must import the re module, which has functions like findall(), search(), split(), and others, in order to use regular expressions in Python.

These were the key advanced Python ideas that any Python developer should be familiar with. You’ll become a better programmer and developer as a result of these, and your code will also be faster and easier to read.


import re

def is_valid_email(email):
    # define a regular expression pattern for a valid email address
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    # check if the given email matches the pattern
    if re.match(pattern, email):
        return True
        return False

res1 = is_valid_email("[email protected]")

res2 = is_valid_email("[email protected]")



In this example, we define a regular expression pattern for a valid email address using a combination of characters and special symbols. The pattern checks for a string that starts with one or more alphanumeric characters, followed by an ‘@’ symbol, followed by one or more alphanumeric characters or hyphens, followed by a period, and ends with two or more alphabets.

We then use Python’s re.match() function to check if the given email string matches the pattern. If it does, we return True, indicating that the email is valid. Otherwise, we return False.

Dunder Method

Dunder (or double underscore) methods, sometimes known as magic methods, are unique categories of functions that are called upon internally. They have two underscores at the beginning and end. Examples of such functions include __add__(), __abs__(), __round__(), __floor__(), __str__(), __trunc__(), and  __lshift__(), among others. Number and number + 5 are equivalent expressions. Other procedures or operations internally invoke the function __add__(5). You can use these functions directly because doing so will result in fewer function calls throughout your code, which will shorten its execution time.


class Car:
    def __init__(self, make, model, year):
        self.make = make
        self.model = model
        self.year = year
    def __str__(self):
        return f"{self.make} {self.model} ({self.year})"
    def __eq__(self, other):
        if isinstance(other, Car):
            return (self.make, self.model, self.year) == (other.make, other.model, other.year)
        return False
print(Car("BWM", "E87", 2010))


BWM E87 (2010)

In this example, we have defined a Car class that has two Dunder Methods: str and eq.

The str method is called when we use the str() function on an instance of the Car class. It returns a string that describes the make, model, and year of the car. The eq method is called when we use the == operator to compare two instances of the Car class. It compares the make, model, and year of the two cars and returns True if they are the same and False if they are different.

In addition to these advanced features, Python also offers a number of performance optimization techniques that can help to improve the speed and efficiency of your code. For example, you can use caching, memoization, and lazy evaluation techniques to reduce the amount of computation required for a given task. You can also use multithreading, multiprocessing, and asynchronous programming techniques to take advantage of modern hardware architectures and improve overall performance.

Python is a versatile and powerful language that offers a wide range of advanced features and capabilities. Whether you are working on a small script or a large-scale software project, Python has the tools and resources you need to write clean, efficient, and maintainable code. By mastering these advanced features, you can take your Python skills to the next level and become a more effective and productive developer.

You will learn about many more such topics in the next section.

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