Python 3: Deep Dive (Part 4

This Python3: Deep Dive Part 4 course takes a closer look at object oriented programming (OOP) in Python.

You must have a strong working knowledge of functional Python programming as well as some practical experience developing Python applications in order to fully benefit from this course.

In-depth functional Python programming
functions, closures, scopes, decorators (using and writing them)
zip, sorted, any, all, and the itertools module in general
sequences, iterables, iterators and generators (what they are and how to implement the corresponding protocols)
generators, yield, and context managers
mapping types, hashing and relation to object equality
some prior knowledge of basic OOP concepts
know how to work with Python virtual environments and pip install
available Jupyter Notebook (freely available) to follow along with the course notebooks
how to use git

[Please note that this is not a cookbook style course - I don't show you how to solve specific problems, but rather a broad and in-depth look at how OOP works in the context of Python, that will allow you to apply these concepts and techniques to your own problems.]

What's inside

Learning objectives

Python object oriented concepts
Classes
Methods and binding
Instance, class and static methods
Properties
Property decorators

Single inheritance
Slots
Descriptors
Enumerations
Exceptions
Metaprogramming

Python object oriented concepts
Classes
Methods and binding
Instance, class and static methods
Properties
Property decorators
Single inheritance
Slots
Descriptors
Enumerations
Exceptions
Metaprogramming

Syllabus

Introduction

Classes are Callables - Lecture

Prerequisites

Course Slides

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores object-oriented programming concepts in Python, which is essential for building complex and maintainable applications, and is a core skill for Python developers

Covers advanced topics such as slots, descriptors, and metaprogramming, which are useful for optimizing code and creating more flexible and dynamic applications, and are useful for experienced developers

Requires a strong working knowledge of functional Python programming and practical experience, so learners should ensure they meet these prerequisites before enrolling, as this course is not for beginners

Includes projects that allow learners to apply the concepts learned, which is a great way to reinforce understanding and develop practical skills, and is useful for building a portfolio

Examines inheritance, polymorphism, and special methods, which are fundamental concepts in object-oriented programming and are essential for writing reusable and maintainable code, and are useful for all developers

Requires familiarity with Python virtual environments and git, which are essential tools for modern software development and collaboration, and are useful for working on team projects

Reviews summary

Deep dive into python oop mechanics

According to learners, this course offers a deep dive into Python's object-oriented programming mechanics, moving significantly beyond basic concepts. Students frequently praise the instructor for his clear explanations of nuanced topics like descriptors, slots, and dunder methods, highlighting the course's thoroughness. It is emphasized that this is part of a series, and meeting the stated prerequisites, particularly a strong foundation in functional Python, is crucial; some reviewers found it challenging without sufficient prior knowledge. The approach is detailed and theoretical, described as focusing on the *why* rather than being a cookbook, with valuable coding projects that help solidify complex understanding.

Deep dive into internal Python OOP workings.

"This course is not a cookbook style course..."

"I wish there were more practical, real-world examples, but the focus is clearly on mechanics."

"It felt a bit academic, but if you want to understand the *why* behind Python OOP, this is the course."

"The course focuses heavily on the underlying mechanisms rather than just how to use OOP."

Hands-on coding exercises are valuable.

"Projects were challenging but valuable."

"Projects were good application."

"The coding exercises really helped me solidify my understanding."

"I found the projects to be a great way to practice the concepts learned in lectures."

Instructor clarifies complex topics effectively.

"Lectures are clear."

"The instructor is top-notch, explaining complex topics patiently."

"As expected from this instructor, it's incredibly thorough and well-explained."

"His explanations on method binding and properties were eye-opening for me."

Covers advanced Python OOP mechanics thoroughly.

"Excellent depth on descriptors and metaprogramming."

"Takes OOP far beyond basic concepts."

"Best explanation of Python OOP I've found. Covers topics like `__slots__` and descriptors with incredible clarity."

"If you want to understand the *why* behind everything in Python OOP, this is it."

"The course is very detailed and covers the subject in depth."

Strong prior Python knowledge is essential.

"Assumes prior knowledge as stated, which is fair."

"I struggled a bit with the prerequisites. The functional stuff mentioned wasn't my strong suit..."

"This course was too advanced for me. I think you really need to understand Python's internals and functional concepts from the previous parts."

"I'd say it definitively requires solid functional Python background before diving into this."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python 3: Deep Dive (Part 4 - OOP) with these activities:

Review Python Fundamentals

Show steps

Solidify your understanding of fundamental Python concepts like data types, control flow, and functions to prepare for the advanced OOP topics covered in this course.

Browse courses on Python Basics

Show steps

Review Python data types (lists, dictionaries, tuples).
Practice writing functions and using control flow statements.
Work through basic Python exercises on platforms like HackerRank or LeetCode.

Review 'Fluent Python'

Show steps

Deepen your understanding of Python's object model and special methods, which are crucial for mastering OOP in Python.

View Fluent Python: Clear, Concise, and Effective... on Amazon

Show steps

Read the chapters on data structures and object-oriented idioms.
Experiment with the examples provided in the book.
Try to apply the concepts to your own projects.

Review 'Effective Python'

Show steps

Learn best practices for writing Pythonic code, including those related to object-oriented programming.

View Effective Python: 125 Specific Ways to Write... on Amazon

Show steps

Read the sections related to classes and inheritance.
Apply the advice to your own projects.
Refactor existing code to follow the best practices.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Build a Simple Game Using OOP

Show steps

Apply your OOP knowledge by building a simple game like Tic-Tac-Toe or a text-based adventure game, focusing on class design and interactions.

Show steps

Plan the game's structure and classes.
Implement the classes and their methods.
Test and debug the game.

Implement OOP Design Patterns

Show steps

Reinforce your understanding of OOP principles by implementing common design patterns like Singleton, Factory, and Observer in Python.

Show steps

Study the theory behind each design pattern.
Implement the pattern in Python.
Apply the pattern to a small project.

Create a Blog Post on Python Descriptors

Show steps

Solidify your understanding of Python descriptors by writing a blog post explaining how they work and their use cases.

Show steps

Research Python descriptors thoroughly.
Write a clear and concise explanation of descriptors.
Include examples of how to use descriptors.
Publish the blog post on a platform like Medium or your own website.

Contribute to an Open Source Python Project

Show steps

Gain practical experience with OOP by contributing to an open-source Python project that utilizes object-oriented principles.

Show steps

Find an open-source Python project on GitHub.
Identify an issue to work on.
Submit a pull request with your changes.

Career center

Learners who complete Python 3: Deep Dive (Part 4 - OOP) will develop knowledge and skills that may be useful to these careers:

Python Developer

A Python developer specializes in creating software using the Python programming language. This course is incredibly helpful to a Python developer, as it provides a very thorough exploration of Python's object oriented programming features, which are central to writing complex applications. The course covers topics such as properties, inheritance, slots, and descriptors, empowering a Python developer to write more sophisticated software. The course's emphasis on practical programming with Jupyter Notebooks, along with git, helps a Python developer hone their skills in real-world development scenarios.

See salaries and explore the career path for Python Developer

Software Developer

A software developer designs, creates, and maintains software applications. This course, with its deep dive into object oriented programming in Python, directly helps a software developer write more efficient, scalable, and maintainable code. The course specifically covers classes, methods, inheritance, and other core OOP concepts, which are essential for any software developer who works with a modern programming language such as Python. The practical emphasis of this course, including projects and coding exercises, helps build a strong foundation in these vital principles. This course helps solidify object oriented concepts, making a software developer more effective in their role.

See salaries and explore the career path for Software Developer

Backend Engineer

A backend engineer builds and maintains the server-side logic that powers web applications. This course helps a backend engineer as object oriented programming is essential for designing and developing robust backend systems. The course's focus on classes, methods, and inheritance enables a backend engineer to create modular, reusable, and scalable code. The course's in-depth look at Python helps a backend engineer working in a Python environment to produce clean concise and performant code. The course's coverage of subjects like descriptors helps a backend engineer be more effective in their role.

See salaries and explore the career path for Backend Engineer

Software Architect

A software architect designs the overall structure of software systems. This course teaches object oriented programming, which is central to software design. The course's in depth study of classes, inheritance, and descriptors, helps a software architect design more scalable and maintainable systems. The course's focus on advanced concepts allows a software architect to create more modular, reusable, and flexible software designs. A software architect can use the principles learned here to build more robust and efficient software systems.

See salaries and explore the career path for Software Architect

Machine Learning Engineer

A machine learning engineer is responsible for developing and deploying machine learning models. This course is useful to a machine learning engineer as it provides an understanding of object oriented programming in Python, essential for creating maintainable and efficient machine learning systems. The course's coverage of classes, methods, and inheritance allows a machine learning engineer to develop complex models with modular components. A machine learning engineer can use the course's material on descriptors and properties to create flexible and reusable machine learning components. The course's project based approach helps a machine learning develop the necessary skills.

See salaries and explore the career path for Machine Learning Engineer

Algorithm Developer

An algorithm developer designs and implements algorithms for various applications, often requiring complex data structures and logical operations. This course teaches object oriented programming in Python, which helps an algorithm developer create reusable and maintainable code. The course's focus on topics such as classes, methods, and inheritance provides a foundation for implementing complex algorithms in Python. With practice in writing Python and using Jupyter Notebooks, this course can be valuable to an algorithm developer who is applying algorithms in practice.

See salaries and explore the career path for Algorithm Developer

Data Scientist

A data scientist analyzes data to derive meaningful insights and build predictive models. While this course may not directly focus on statistical analysis, the object oriented concepts it teaches provides a data scientist with the ability to write more maintainable and scalable data processing pipelines and machine learning models. A data scientist can use Python to create custom data structures using classes and objects. The course's coverage of inheritance, for example, can allow a data scientist to create specialized machine learning models based on more general base classes. This course enables a data scientist to write more efficient, effective Python code.

See salaries and explore the career path for Data Scientist

Full-Stack Developer

A full stack developer works on both the front-end and back-end of web applications. This course helps a full stack developer who also works on the backend with Python. The course's detailed coverage of object oriented programming empowers a full stack developer to create well structured, maintainable Python code for the server side. Topics, such as classes, methods, and inheritance, helps a full stack developer design reusable and scalable components. For a full stack developer working with python, this course may be useful in improving a development workflow.

See salaries and explore the career path for Full-Stack Developer

Research Scientist

A research scientist conducts scientific research, often involving complex data analysis and simulations. The object oriented programming principles this course provides helps a research scientist structure their code more effectively. The course's coverage of advanced OOP concepts helps a research scientist write more sophisticated simulations, which often require the use of classes and inheritance. The experience this course provides with Python may be useful to a research scientist who uses Python or similar languages in their research.

See salaries and explore the career path for Research Scientist

Data Engineer

A data engineer builds and manages the infrastructure for data storage and processing. While this course focuses on object oriented programming, it can help a data engineer develop more efficient and maintainable data pipelines using Python. The course content on classes, interfaces, and inheritance helps a data engineer build robust data processing workflows. This course may be useful for a data engineer using Python or similar languages in their work, especially in the creation of custom data structures.

See salaries and explore the career path for Data Engineer

Robotics Engineer

A robotics engineer designs, builds, and programs robots. This course explores object oriented programming in Python, which is beneficial for writing modular and maintainable robot control systems. The course's focus on classes and inheritance helps a robotics engineer organize complex robot behaviors into reusable components. If a robotics engineer is using Python, then this course will help make them more effective through object oriented programming.

See salaries and explore the career path for Robotics Engineer

Quantitative Analyst

A quantitative analyst develops and implements mathematical and statistical models for financial markets. This course, while focused on object oriented programming in Python, may be useful in creating custom tools to process and analyze quantitative financial data. The course's material on classes and inheritance can allow a quantitative analyst to create custom classes tailored to specific financial instruments or data structures. The Python programming skills this course provides may be valuable in a quantitative analyst's day to day work.

See salaries and explore the career path for Quantitative Analyst

Game Developer

A game developer creates the software for video games. This course's focus on object oriented programming helps a game developer organize game logic into reusable components and manage complexity. The course's coverage of classes, inheritance, and methods allows a game developer to build efficient and scalable game systems. Game developers may find this course to be beneficial, especially if they are working with Python.

See salaries and explore the career path for Game Developer

Embedded Systems Engineer

An embedded systems engineer designs software for embedded systems. Object oriented programming principles, as taught in this course, help an embedded systems engineer write more organized and maintainable code. The course's focus on classes, inheritance, and other OOP concepts enables an embedded systems engineer to break complex system logic into modular components. This course may be useful for an embedded systems engineer, especially if they are working with Python or languages that support OOP.

See salaries and explore the career path for Embedded Systems Engineer

DevOps Engineer

A DevOps engineer automates and streamlines software development and deployment processes. While this course focuses on object oriented programming in Python, this may be useful for a DevOps engineer who is using Python to automate infrastructure tasks or to perform configuration management. A DevOps engineer may use their programming knowledge to create tools to automate tasks. The course on object oriented concepts may be beneficial to the DevOps Engineer.

See salaries and explore the career path for DevOps Engineer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python 3: Deep Dive (Part 4 - OOP).

Fluent Python

Save

Provides a deep dive into Python's core features, including many aspects of OOP. It's particularly useful for understanding how Python's data model and special methods enable powerful and expressive code. It serves as an excellent reference for understanding the nuances of Python's object model and how it differs from other languages. This book expands on the course material by providing practical examples and in-depth explanations of advanced Python concepts.

Fluent Python: Clear, Concise, and Effective...

Paperback

Fluent Python: Clear, Concise, and Effective...

Effective Python

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Offers practical advice on writing clean, efficient, and Pythonic code. While not solely focused on OOP, it covers many best practices relevant to object-oriented design in Python. It valuable resource for improving your overall Python programming skills and writing more maintainable code. This book provides additional depth to the course by offering concrete examples and actionable advice on how to apply OOP principles effectively.

Effective Python: 125 Specific Ways to Write Better...

Kindle Edition

Python 3

Deep Dive (Part 4 - OOP)

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Deep dive into python oop mechanics

Activities

Career center

Reading list

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