Advanced Python Tutorials

Advanced Python: You’re an experienced Python programmer, ready to advance your skills even further. While you know basic and intermediate Python, there are a multitude of powerful, more sophisticated concepts to master to become an expert Python dev. That’s where our comprehensive Advanced Python course Python DSA Course on PW skills comes in. We will be your guide to truly advanced techniques like concurrency and parallelism, which allow you to speed up your programs by running operations simultaneously instead of sequentially.

You will learn metaclasses and other metaprogramming methods that customize and even generate code for you automatically at runtime, saving you coding time in large projects. We’ll cover optimizing Python code—both to reduce memory usage allowing your programs to scale and to reduce overall execution time.

Moreover, you will gain expertise in testing methodologies like unit testing, integration testing, and test-driven development to produce bug-free, trustworthy code. Finally, we will show you how to package Python code for production deployment, so you can share your programs professionally with others.

Enroll now to master advanced Python and reach new heights in your Python programming.

Table of Contents

Advanced Python: The Comprehensive Definition!

Advanced Python refers to the utilization of sophisticated techniques, libraries, and design patterns to tackle complex problems and optimize performance in Python programming. It encompasses mastery of the language’s advanced features, such as generators, decorators, metaprogramming, concurrency, and asynchronous programming.

Advanced Python developers possess a deep understanding of Python’s internals, enabling them to create efficient, scalable, and maintainable solutions for diverse domains.

Key Components and Concepts!

Generators: Generators are functions that yield results one at a time, allowing for memory-efficient iteration over large datasets. Advanced Python programmers leverage generators to process streams of data or to implement custom iteration patterns.

Decorators: Decorators are higher-order functions that modify the behavior of other functions or methods. Advanced Python developers employ decorators to add functionalities like caching, logging, or access control to existing code without altering its structure.

Metaprogramming: Metaprogramming involves writing code that manipulates Python code at runtime. Techniques like dynamic class creation, modifying functions or classes, and using metaclasses empower advanced Python developers to build flexible and dynamic systems.

Concurrency: Python offers multiple approaches to concurrency, including threads, multiprocessing, and asynchronous programming with asyncio. Advanced Python programmers understand the trade-offs between these approaches and employ them judiciously to write highly concurrent and scalable applications.

Asynchronous Programming: Asynchronous programming enables concurrent execution of multiple tasks without explicit threading or multiprocessing. Advanced Python developers utilize asynchronous frameworks like asyncio to build responsive and scalable network applications, leveraging features such as coroutines and event loops.

Pythonic Idioms: Advanced Python coding involves adhering to Pythonic idioms and best practices to write clear, concise, and efficient code. This includes utilizing list comprehensions, context managers, and duck typing to express ideas succinctly and elegantly.

Performance Optimization: Profiling, caching, and algorithmic optimization are crucial aspects of advanced Python programming. Developers employ techniques like memoization, algorithmic improvements, and optimizing data structures to enhance the performance of their Python applications.

Integration with C/C++ Extensions: In performance-critical scenarios, integrating Python with C/C++ extensions using tools like Cython or ctypes becomes essential. Advanced Python developers are proficient in interfacing Python with native code to achieve significant performance gains.

Concurrency Control: Advanced Python developers understand the complexities of concurrent programming and employ techniques like locks, semaphores, and atomic operations to ensure thread safety and prevent race conditions in multithreaded or multiprocessing environments.

Unit Testing and Debugging: Rigorous testing and debugging are integral parts of advanced Python development. Developers employ unit testing frameworks like pytest and sophisticated debugging tools like pdb or PyCharm to ensure the correctness and reliability of their code.

In short, advanced Python programming entails mastering a wide array of advanced language features, libraries, and techniques to write efficient, scalable, and maintainable code for diverse applications and domains. It requires a deep understanding of Python’s internals, strong problem-solving skills, and adherence to best practices and design patterns.

Why Study Advanced Python?

Studying advanced Python offers numerous benefits for both beginner and experienced programmers alike. Whether you’re looking to enhance your skill set, advance in your career, or tackle more complex programming challenges, delving into advanced Python concepts is a wise investment of time and effort.

Career Advancement

Advanced Python skills are highly sought after in the job market. Employers often require expertise in advanced Python concepts for roles in software development, data science, machine learning, web development, and more. By mastering advanced Python, you increase your competitiveness and open up new career opportunities. For example, knowledge of asynchronous programming with asyncio can be particularly valuable for developers working on high-performance web servers or network applications.

Problem-Solving Capabilities

Advanced Python equips you with a diverse set of tools and techniques to tackle complex problems more effectively. For instance, understanding closures and decorators allows you to create reusable and modular code, enhancing code maintainability and scalability. Moreover, proficiency in regular expressions enables you to manipulate and process textual data efficiently, which is essential in tasks such as data validation, parsing, and text mining.

Performance Optimization

Learning advanced Python enables you to optimize the performance of your applications. For example, mastering memory management techniques helps you minimize memory leaks and improve resource utilization, leading to faster and more efficient programs. Additionally, understanding iterators, generators, and list comprehensions allows you to write code that operates more efficiently on large datasets, enhancing the overall performance of your applications.

Robust Testing Practices

Advanced Python knowledge empowers you to write comprehensive test suites that ensure the reliability and correctness of your code. By understanding unit testing frameworks like pytest and applying testing best practices, you can identify and fix bugs early in the development process, saving time and resources in the long run. For instance, writing test cases for different scenarios in a web application ensures that the application functions as expected under various conditions, enhancing its quality and reliability.

Code Maintainability and Reusability

Advanced Python concepts promote code maintainability and reusability, essential aspects of software development. For example, object-oriented programming (OOP) allows you to encapsulate data and behavior within classes, facilitating code organization and modular design. Similarly, closures and decorators enable you to add functionality to existing code without modifying its structure, promoting code reuse and extensibility.

Innovation and Creativity

Mastering advanced Python encourages innovation and creativity in your programming endeavors. With a solid understanding of advanced concepts like metaprogramming and concurrency control, you can explore novel solutions to complex problems and develop innovative applications. For example, leveraging concurrency techniques enables you to build responsive and scalable applications that can handle multiple tasks simultaneously, opening up possibilities for new and exciting projects.

Recommended Technical Course

Advanced Python Tutorial For Beginners

Welcome to the Advanced Python Tutorial designed specifically for beginners looking to expand their Python skills beyond the basics. In this comprehensive guide, we will delve into advanced Python topics and sub-topics, providing detailed explanations, examples, and practical applications to help you master these concepts with ease.

Advanced Conditional Statements

Conditional statements are fundamental in programming, and mastering advanced conditional statements in Python can significantly enhance your coding capabilities.

We’ll explore:

Example:

# Advanced conditional statement example

x = 10

y = 5

result = “x is greater than y” if x > y else “x is less than or equal to y”

print(result)

List Comprehension

List comprehension is a concise way to create lists in Python. It allows you to generate lists using a single line of code, making your code more readable and efficient. We’ll cover:

Example:

# List comprehension example

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

print(squares)

Python Dictionary Comprehension

Similar to list comprehension, dictionary comprehension provides a compact way to create dictionaries in Python. It offers a concise syntax for creating dictionaries based on existing iterables. We’ll discuss:

Example:

# Dictionary comprehension example

numbers = [1, 2, 3, 4, 5]

squared_dict = {num: num**2 for num in numbers}

print(squared_dict)

Lambda Operator

Lambda functions, also known as anonymous functions, are small, one-line functions defined using the lambda keyword. They are useful for creating quick functions without explicitly defining them using def. We’ll explore:

Example:

# Lambda function example

addition = lambda x, y: x + y

print(addition(3, 4))

Filter

The filter() function in Python is used to filter elements from an iterable based on a given function. It returns an iterator that yields those items of the iterable for which the function returns true. We’ll cover:

Example:

# Filter function example

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

even_numbers = list(filter(lambda x: x % 2 == 0, numbers))

print(even_numbers)

Reduce

The reduce() function is used to apply a function to an iterable and reduce it to a single cumulative value. It continually applies the function to the elements and returns a single value. We’ll discuss:

Example:

# Reduce function example

from functools import reduce

numbers = [1, 2, 3, 4, 5]

product = reduce(lambda x, y: x * y, numbers)

print(product)

Map

The map() function in Python is used to apply a function to all the elements of an iterable and return an iterator that yields the results. It helps to perform element-wise operations efficiently. We’ll explore:

Example:

# Map function example

numbers = [1, 2, 3, 4, 5]

squared_numbers = list(map(lambda x: x**2, numbers))

print(squared_numbers)

Recursive Functions

Recursive functions are functions that call themselves during their execution. They are particularly useful for solving problems that can be broken down into smaller, similar sub-problems. We’ll cover:

Example:

# Recursive function example (Factorial)

def factorial(n):

if n == 0:

return 1

else:

return n * factorial(n-1)

print(factorial(5))

Regular Expressions

Regular expressions (regex) are powerful tools for pattern matching and text manipulation. They provide a concise and flexible means to search, match, and manipulate strings. We’ll explore:

Example:

# Regular expression example (Matching email addresses)

import re

text = “Contact us at email@example.com for more information”

email = re.search(r'[\w\.-]+@[\w\.-]+’, text)

print(email.group(0))

Advanced Regular Expressions

Building upon the basics of regular expressions, advanced regular expressions delve into more complex patterns and techniques for sophisticated text processing tasks. We’ll discuss:

Example:

# Advanced regular expression example (Extracting domain names from URLs)

import re

urls = [“http://www.example.com”, “https://www.test.com”]

domains = [re.search(r’https?://([\w\.-]+)’, url).group(1) for url in urls]

print(domains)

Check if a String Starts and Ends with the Same Character or Not

This task involves checking if a string starts and ends with the same character, which can be achieved using string slicing or regular expressions. We’ll provide examples for both methods.

Example:

# Check if string starts and ends with the same character

def starts_ends_same_char(s):

return s[0] == s[-1]

print(starts_ends_same_char(“abcba”))

# Output: True

Password Validation in Python

Password validation is a common task in web development and security-related applications. We’ll discuss various criteria for password validation and how to implement them in Python.

Example:

# Password validation example

import re

def is_valid_password(password):

# At least 8 characters, one uppercase letter, one lowercase letter, one number, and one special character

regex = r”^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[@$!%*?&])[A-Za-z\d@$!%*?&]{8,}$”

return re.fullmatch(regex, password) is not None

print(is_valid_password(“Passw0rd!”))

# Output: True

Extract Email Addresses Using Regular Expressions in Advanced Python

Extracting email addresses from a text or a document is a common task in data processing and analysis. We’ll demonstrate how to accomplish this using regular expressions in Python.

Example:

# Extracting email addresses from text

import re

text = “Contact us at email@example.com or support@example.org for assistance”

emails = re.findall(r'[\w\.-]+@[\w\.-]+’, text)

print(emails)

Validating UPI IDs Using Regular Expressions

Unified Payments Interface (UPI) IDs are unique identifiers used for peer-to-peer transactions in India. We’ll discuss how to validate UPI IDs using regular expressions in Python.

Example:

# Validating UPI IDs

import re

def is_valid_upi(upi):

# UPI ID format: alphanumeric characters with optional period, underscore, and hyphen

regex = r’^[\w.-]+@[A-Za-z\d.-]+$’

return re.fullmatch(regex, upi) is not None

print(is_valid_upi(“example123@upi”))

# Output: True

Read More: 30-Days-Of-Python

OOPS (Object-Oriented Programming)

Class and Object in Python: In Python, a class is a blueprint for creating objects. Objects are instances of classes. Here’s a simple example:

class Car:

def __init__(self, brand, model):

self.brand = brand

self.model = model

# Creating an object of Car class

my_car = Car(“Toyota”, “Camry”)

print(my_car.brand) # Output: Toyota

print(my_car.model) # Output: Camry

Inheritance: Inheritance allows a class to inherit attributes and methods from another class.

Example:

class ElectricCar(Car): # ElectricCar inherits from Car

def __init__(self, brand, model, battery_capacity):

super().__init__(brand, model)

self.battery_capacity = battery_capacity

tesla = ElectricCar(“Tesla”, “Model S”, “100 kWh”)

print(tesla.brand) # Output: Tesla

print(tesla.model) # Output: Model S

print(tesla.battery_capacity)

# Output: 100 kWh

Encapsulation: Encapsulation restricts access to some of the object’s components. This can prevent the accidental modification of data.

Example:

class BankAccount:

def __init__(self):

self.balance = 0

def deposit(self, amount):

self.balance += amount

def withdraw(self, amount):

if self.balance >= amount:

self.balance -= amount

return amount

else:

return “Insufficient funds”

account = BankAccount()

account.deposit(1000)

account.withdraw(500)

# Output: 500

Polymorphism: Polymorphism allows methods to do different things based on the object that it’s acting upon.

Example:

class Animal:

def sound(self):

pass

class Dog(Animal):

def sound(self):

return “Woof”

class Cat(Animal):

def sound(self):

return “Meow”

def make_sound(animal):

return animal.sound()

dog = Dog()

cat = Cat()

print(make_sound(dog)) # Output: Woof

print(make_sound(cat)) # Output: Meow

Data Abstraction: Data abstraction refers to the concept of hiding internal implementation details and showing only the necessary features of an object.

Example:

from abc import ABC, abstractmethod

class Shape(ABC):

@abstractmethod

def area(self):

pass

class Rectangle(Shape):

def __init__(self, width, height):

self.width = width

self.height = height

def area(self):

return self.width * self.height

rectangle = Rectangle(5, 10)

print(rectangle.area())

# Output: 50

Operator Overloading: Operator overloading allows defining custom behavior for built-in operators.

Example:

class Vector:

def __init__(self, x, y):

self.x = x

self.y = y

def __add__(self, other):

return Vector(self.x + other.x, self.y + other.y)

v1 = Vector(2, 4)

v2 = Vector(1, 3)

result = v1 + v2

print(result.x, result.y)

# Output: 3 7

ITERABLES, ITERATORS, and Generators

Iterable vs Iterator: An iterable is an object capable of returning its members one at a time, whereas an iterator is an object used to iterate over an iterable.

Example:

my_list = [1, 2, 3]

iter_list = iter(my_list) # Creating an iterator from an iterable

print(next(iter_list)) # Output: 1

print(next(iter_list))

# Output: 2

Generators: Generators are a simple way to create iterators using functions. They allow you to iterate over a set of items without constructing the entire object at once.

Example:

def countdown(n):

while n > 0:

yield n

n -= 1

for num in countdown(5):

print(num)

# Output: 5, 4, 3, 2, 1

CLOSURES and DECORATORS

Closures: A closure is a function that retains the bindings of the free variables that exist when the function is defined.

Example:

def outer_function(x):

def inner_function(y):

return x + y

return inner_function

add_five = outer_function(5)

print(add_five(3))

# Output: 8

Simple Decorator: A decorator is a function that takes another function as an argument and extends its behavior without modifying it.

Example:

def decorator(func):

def wrapper():

print(“Before function execution”)

func()

print(“After function execution”)

return wrapper

@decorator

def greet():

print(“Hello”)

greet()

# Output: Before function execution, Hello, After function execution

Decorator with Arguments: Decorators can also accept arguments.

Example:

def repeat(num_times):

def decorator(func):

def wrapper(*args, **kwargs):

for _ in range(num_times):

result = func(*args, **kwargs)

return result

return wrapper

return decorator

@repeat(num_times=3)

def greet(name):

print(f”Hello, {name}”)

greet(“Alice”)

# Output: Hello, Alice (printed 3 times)

Class as Decorator: A class can also be used as a decorator by implementing the __call__ method.

Example:

class Decorator:

def __init__(self, func):

self.func = func

def __call__(self, *args, **kwargs):

print(“Before function execution”)

self.func(*args, **kwargs)

print(“After function execution”)

@Decorator

def greet():

print(“Hello”)

greet()

# Output: Before function execution, Hello, After function execution

Memory Management

Memory Management: Memory management in Python is handled by the Python Memory Manager. It handles the allocation and deallocation of memory for Python objects. Python uses a private heap to manage memory.

Garbage Collection: Python uses a technique called garbage collection to automatically manage memory. Garbage collection refers to the process of automatically identifying and deleting objects that are no longer needed, freeing up memory for other uses.

Example:

class MyClass:

def __init__(self, name):

self.name = name

# Creating an instance of MyClass

obj1 = MyClass(“Object 1”)

obj2 = MyClass(“Object 2”)

# Deleting obj1

del obj1

# At this point, obj1 is no longer accessible and will be garbage-collected

Reference Counting: Python uses a technique called reference counting to keep track of how many references there are to an object. When the reference count drops to zero, meaning there are no more references to the object, Python automatically deallocates the memory associated with that object.

Example:

x = 10 # Reference count of the integer object 10 is 1

y = x # Reference count becomes 2

z = x # Reference count becomes 3

del x # Reference count becomes 2

del y # Reference count becomes 1

del z # Reference count becomes 0, memory is deallocated

Dynamic Typing: In Python, variables are dynamically typed, meaning you can reassign variables to different data types.

Example:

x = 10 # x is an integer

x = “hello” # Now x is a string

Mutable & Immutable Objects:

Mutable objects can be modified after creation, while immutable objects cannot be changed.

Example:

# Mutable object (list)

mutable_list = [1, 2, 3]

mutable_list.append(4) # Modifying the list

print(mutable_list) # Output: [1, 2, 3, 4]

# Immutable object (tuple)

immutable_tuple = (1, 2, 3)

# immutable_tuple.append(4) # This will raise an AttributeError since tuples are immutable

Memory Profiling: Memory profiling is the process of analyzing memory usage in a program to identify areas where memory is being used inefficiently.

Example:

import tracemalloc

tracemalloc.start()

# Code to profile

# …

snapshot = tracemalloc.take_snapshot()

top_stats = snapshot.statistics(‘lineno’)

for stat in top_stats:

print(stat)

Deep Copy vs Shallow Copy: In Python, copying objects can be done using deep copy or shallow copy. Deep copy creates a new object and recursively adds copies of objects found in the original. Shallow copy creates a new object and inserts references to objects found in the original.

Example:

import copy

# Shallow copy

original_list = [[1, 2, 3], [4, 5, 6]]

shallow_copy = copy.copy(original_list)

# Deep copy

deep_copy = copy.deepcopy(original_list)

# Modifying the original list

original_list[0][0] = 100

print(original_list) # Output: [[100, 2, 3], [4, 5, 6]]

print(shallow_copy) # Output: [[100, 2, 3], [4, 5, 6]]

print(deep_copy) # Output: [[1, 2, 3], [4, 5, 6]]

Optimization Tips for Python Code: Some general tips for optimizing Python code include using built-in functions, avoiding unnecessary loops, using data structures efficiently, and leveraging libraries and modules.

Example:

# Using list comprehension instead of loops

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

# Using built-in functions

max_value = max(squares)

# Avoiding unnecessary operations

if x in my_list: # Avoid using ‘my_list.index(x)’ if you don’t need the index

pass

How Python Stores Integers in Memory: Python uses a fixed-size representation for integers, which means integers are stored in a fixed number of bytes regardless of their size.

Example:

import sys

x = 10

print(sys.getsizeof(x)) # Output: 28 bytes (on a 64-bit system)

Testing

Testing with Pytest: Pytest is a testing framework for Python that makes it easy to write simple and scalable test cases.

Example:

# test_calc.py

def test_addition():

assert 1 + 2 == 3

def test_subtraction():

assert 5 – 3 == 2

To run the tests:

pytest test_calc.py

DocTests: DocTests are tests that are embedded in the documentation of a Python function or module.

Example:

# my_module.py

def add(x, y):

“””

This function adds two numbers.

>>> add(2, 3)

>>> add(-1, 1)

“””

return x + y

To run DocTests:

python -m doctest my_module.py

UnitTests: UnitTests are a built-in testing framework in Python for writing and running tests.

Example:

import unittest

def add(x, y):

return x + y

class TestAddition(unittest.TestCase):

def test_addition(self):

self.assertEqual(add(1, 2), 3)

self.assertEqual(add(-1, 1), 0)

if __name__ == ‘__main__’:

unittest.main()

To run UnitTests:

python test_addition.py

Master advanced Python with our comprehensive course. Go beyond basics with Python DSA Course on PW skills and learn OOP, regex, and generators to advanced techniques for concurrency, performance optimization, robust testing, and production deployment.

Become an expert Python developer through real-world examples and hands-on exercises. Take your Python skills to new heights and open up more job opportunities.

Enroll now and get lifetime access at an exclusive discounted price. Accelerate your Python journey today.

For Latest Tech Related Information, Join Our Official Free Telegram Group : PW Skills Telegram Group

Advanced Python FAQs

What is the difference between shallow copy and deep copy in Python?

Deep copy creates a new object and recursively adds copies of objects found in the original, while shallow copy creates a new object and inserts references to objects found in the original. Deep copy creates fully independent copies, while shallow copy creates copies that still share some references.

How does Python's garbage collection work, and when does it occur?

Python's garbage collection automatically identifies and deletes objects that are no longer needed, freeing up memory for other uses. It uses reference counting and a cyclic garbage collector. Garbage collection occurs periodically or when triggered by certain conditions, such as reaching a memory threshold.

What are closures and decorators in Python, and how are they used?

Closures are functions that have access to variables from their enclosing scope, even after the scope has finished executing. Decorators are functions that modify the behavior of other functions or methods. Closures are created by defining a nested function, while decorators are created by prefixing a function with the @decorator_name syntax.

What are the benefits of using iterators and generators in Python?

Iterators and generators provide memory-efficient ways to iterate over large data sets. Iterators allow you to iterate over a sequence of items one at a time, while generators enable you to generate values lazily, as they are needed, conserving memory. They are particularly useful for processing large files or streams of data.

How can I optimize Python code for better performance?

Python code can be optimized by using built-in functions and libraries, avoiding unnecessary loops or operations, optimizing data structures, and implementing caching or memoization where appropriate. Profiling tools can help identify bottlenecks in the code for further optimization. Additionally, utilizing concurrency and parallelism techniques can improve performance for certain tasks.