Book Image

Python – Complete Python, Django, Data Science and ML Guide [Video]

By : Bogdan Stashchuk
5 (2)
Book Image

Python – Complete Python, Django, Data Science and ML Guide [Video]

5 (2)
By: Bogdan Stashchuk

Overview of this book

Join us on an immersive Python programming journey, spanning over 50 hours of learning. Whether you're a novice or experienced, this course equips you with vital Python skills for careers and projects. Starting from the basics, grasp Python's core principles and proficiency in real-world functions. As Python's popularity grows, this course readies you for the rising demand for Python developers. You'll practice hand-on examples using Python's interpreter and Visual Studio Code with Code Runner to solidify your skills. With a focus on Data Science and Machine Learning, you'll master essential packages such as NumPy, Pandas, Matplotlib, and Scikit-learn, using the versatile Jupyter Notebook. The course extensively covers Python's fundamental aspects, spanning variables, lists, dictionaries, and venturing into advanced topics like classes, loops, modules, and creating virtual environments. The goal is to provide you with a solid Python foundation. You'll also gain insight into functional and object-oriented Python programming, making you a versatile coder. The course is thoughtfully structured, explaining not just "how" but also "why" we use specific methods and best practices. By course end, you'll harness Python's full potential for web and mobile app development, data science, machine learning, and game creation.
Table of Contents (91 chapters)
58
Math Module and Recursive Functions
Chapter 21
Modifying Objects in Python
Content Locked
Section 4
Practice - Creating Deep Copies of Objects
In this video, we delve into creating deep copies of objects in Python. You'll learn to use the deep copy function from the copy module, allowing you to create a true copy of an object, including all inner mutable objects. Deep copies ensure that changes to one variable don't affect the other, as demonstrated with dictionaries and their nested lists. Regular copying results in shared references, while deep copying guarantees independence.