Python – all you need to know from basic to advanced 

Here’s a learning roadmap for Python programming that spans basic, intermediate, and advanced levels. Remember that the progression might vary depending on your current skill level and how much time you can dedicate to learning. Feel free to adapt this roadmap to your own pace and goals.

1. Basic Level/ Beginner Level

Introduction to Python:
Learn about Python’s history, syntax, and its role in the programming world.

Setting Up Your Environment:
Install Python and a code editor (Python ide e.g., Visual Studio Code, PyCharm).
Learn how to run Python scripts.

Basic Syntax
Basic Syntax, Print, Input and Comments

Data Types:
Understand Python variables, Different Data Types and Constants (integers, floats, strings, booleans), and basic operations.
Learn about print statements and basic input/output.
Python swapping, Python type casting

Operators
All Kinds of Built Operators in Python & Its Usage

Control Flow/Structures:
Understand Control Structures, Different Types of Conditions & Loops
Explore if statements, loops (for and while), and the concept of indentation.
Practice using conditional statements and loops.

Functions:
Learn how to define and call functions.
Understand parameters, arguments, and return values.

Lists and Dictionaries:
Explore data structures like lists and dictionaries.
Learn about indexing, slicing, and basic manipulation.

List
Python List Data Structure, Built in Methods & Its Operations

Dictionary
Python Dictionary, Accessing Dictionary and All Basic Operations

Tuple & Set
Understand The Necessity of Tuple & Set in Python

String Manipulation:
Understand string methods and formatting.
Learn about regular expressions for pattern matching.
String and Its Built Methods in Python

Error Handling:
Learn about exceptions and how to handle errors using try-except blocks.

2. Intermediate Level

We Know How to Write Code, I Want to Write Better Code

Function
Python Function, Arguments, Anonymous Functions and Function Scopes, Zip function, Recursion 

Exceptions
Error Handling, Exceptions and Exception Handling in Python

Module Package & PIP
Understand Modules & Packages in Python 

Modules and Libraries:
Explore Python’s standard library and third-party libraries.
Learn how to install and use packages using tools like pip.

OOP
Object Oriented Programming, Class, Object and Inheritance in Python

Iterator
Understand Iterator & Generator and Its Implementation in Python

Standard Library
Explore The Built-in Standard Library of Python

File Handling:
Understand reading from and writing to files.
Learn about different file formats (e.g., CSV, JSON).
File opening

Object-Oriented Programming (OOP):
Learn about classes, objects, methods, and attributes.
Understand encapsulation, inheritance, and polymorphism.

Advanced-Data Structures:
Study tuples, sets, and more advanced data structures.
Learn about data manipulation libraries like NumPy and Pandas.

Exception Handling:
Dive deeper into exception-handling techniques and best practices.

Debugging and Testing:
Learn debugging techniques using tools like breakpoints and print statements.
Explore unit testing using frameworks like pytest.

Functional Programming:
Understand functional programming concepts like lambda functions and map/reduce/filter.

Input & Output
File Read Write Operation, Standard Input Output and Stream Buffer Operations

Python math, Pythpn regex, Search and replace, Meta characters, Python try-except, Methods, Constructors, Overwriting, Debugging, Lamda

3. Advanced Level

Actually, I Know Nothing, There is a Lot More to Explore

Concurrency and Multithreading:
Learn about threading, multiprocessing, and asynchronous programming using asyncio.
Decorators and Metaprogramming:
Explore advanced topics like decorators, context managers, and metaclasses.
Understand Concurrency, Parallelism, Multi-Threading & Multi-Processing in Python

Advanced OOP:
Study design patterns and more advanced OOP concepts.
Learn about SOLID principles and their application.

Performance Optimization:
Dive into profiling and optimizing code for better performance.
Learn about memory management and efficient algorithms.

Virtual Environment
Understand & Create a Virtual Environment and Install Third-Party Packages

Thread Based Parallelism
Understand Thread Objects, Lock, RLock, Condition, Semaphore, Event Objects.

Process Based Parallelism
Understand Process Class, Context, Start Methods & Process Synchronizations

Sub Process Management
Subprocess Module, Popen, Frequently Used Arguments and Exceptions

Launching Parallel Tasks
Executor Objects, Thread Pool, Process Pool & Future Objects

Asynchronous 1/0 High Level
Understand Coroutines, Tasks, Awaitables, Futures, Sleeping and Timeouts.

Asynchronous 1/0 Low Level
Understand Event Loop, Scheduling Callbacks & Creating Futures and Tasks

Web Development:
Learn about web frameworks like Django or Flask for building web applications.

Data Science and Machine Learning:
Explore libraries like scikit-learn and TensorFlow for data analysis and machine learning.

Advanced Libraries:
Dive deeper into libraries like NumPy, Pandas, and Matplotlib.

Advanced Topics:
Explore advanced topics like metaprogramming, networking, and security.

What Next?

Now, We have Some Idea About Python. So, What Next?

Remember that consistent practice, working on projects, and seeking out real-world applications of what you’ve learned will greatly enhance your understanding and skills in Python.

More Related Articles

Web development using Python
Data Science and Analysis using Python 
Machine Learning and AI using Python 
Scripting and Automation using Python
Scientific Computing using Python
Game Development using Python
Educational purposes using Python step by step
Rapid Prototyping using Python 
Natural Language Processing using Python
IoT and Embedded Systems using Python
Desktop Applications using Python 
Software Testing using Python
Mobile Development using Python
Networking Development using Python 
Computer Graphics using Python
Backend Development using Python
Robotics learning steps using Python 

Learning robotics using Python can be an exciting journey! Here are the steps you can follow to get started with robotics using Python:

Learn Python Basics:

Make sure you have a solid understanding of Python programming. You should be comfortable with concepts like variables, data types, loops, conditionals, functions, and object-oriented programming (OOP).

Understand Robotics Concepts:

Familiarize yourself with basic robotics concepts, such as kinematics, sensors, actuators, and control systems. Understanding the fundamentals of robotics will provide a strong foundation for your programming efforts.

Choose a Robotics Platform:

Decide whether you want to work with physical robots or simulators. There are various robotics platforms available, such as Arduino, Raspberry Pi, ROS (Robot Operating System), and various simulator environments like Gazebo and V-REP.

Learn Robotics Libraries:

Depending on your chosen platform, explore relevant libraries and frameworks. For example, if you’re using ROS, learn its Python API. If you’re working with hardware like Raspberry Pi or Arduino, learn how to interact with sensors and actuators using libraries like RPi.GPIO or PySerial.

Start with Simple Projects:

Begin with simple robotics projects to apply your programming skills. For instance, create a program that moves a robot forward using motors or reads data from sensors like ultrasonic distance sensors.

Study Control Systems:

Delve into control theory, which is essential for designing algorithms that govern a robot’s behavior. Learn about PID (Proportional-Integral-Derivative) controllers and other control strategies.

Experiment with Sensors and Actuators:

Work with various sensors (e.g., ultrasonic, infrared, camera) and actuators (e.g., motors, servos) to understand how to gather data from the environment and control the robot’s movements.

Explore Vision and Perception:

If you’re interested in computer vision, learn how to process images and videos to extract useful information. Libraries like OpenCV can be extremely helpful in this area.

Simulate Robots:

If you’re using simulation environments like Gazebo, practice simulating robots and experimenting with their behaviors. This is a safe way to test and refine your algorithms before deploying them on physical robots.

Advance to Complex Projects:

Gradually work on more complex projects that involve tasks like autonomous navigation, obstacle avoidance, path planning, and even multi-robot coordination.

Study ROS (If Applicable):

If you’re working with ROS, dive deeper into its concepts, architecture, and tools. ROS offers a wide range of resources and tutorials for learning.

Learn Machine Learning and AI (Optional):

If you’re interested in adding AI capabilities to your robots, explore machine learning and AI techniques that enable tasks like object recognition, natural language processing, and decision-making.

Join Robotics Communities:

Engage with robotics communities, forums, and social media groups. These communities provide a platform to share your work, ask questions, and learn from others.

Stay Updated and Experiment:

Robotics is an evolving field. Keep learning about new technologies and techniques, and continue experimenting with your own projects to refine your skills.

Remember that robotics is a multidisciplinary field, so don’t hesitate to explore related areas like electronics, mechanics, and physics to gain a holistic understanding of how robots work.

Certainly! Python is a versatile programming language that can be used in various fields to make money. 

Here are 10 amazing ways you can make money as a Python programmer:

01. Job/ Remote job/ Freelancing 

Web development 

1.1 Data Analysis and Visualization: 

Analyze and visualize data for companies using Python libraries like pandas, NumPy, and Matplotlib. Businesses often need help in making sense of their data to drive informed decisions.

1.2 Machine Learning and AI: 

Develop machine learning models and AI applications using libraries like TensorFlow or PyTorch. Offer predictive analytics, natural language processing, or image recognition solutions to clients.

1.3 E-commerce Solutions: 

Build custom e-commerce platforms using Python and frameworks like Django. You can help businesses set up online stores and manage their inventory, payments, and orders.

1.4 Automating Tasks: 

Help businesses streamline their processes by automating repetitive tasks using Python scripts. This can include data entry, report generation, or social media management.

Example automation applications:

Stock trading bot – Risky as hell but can be done

Web application template engine

Business report generator

Twitter or Instagram Bots

Sports match-up analyzers

SEO App

1.5 Freelancing or Remote Job (Software Developer)

02. Sass product 

2.1 Create chatbot

2.2 Create a Marketing tool

2.3 Create a Management System 

2.4. building a website/app (monthly income generate and selling it 40 times price)

2.5 Plugins Development & Selling 

03. As an educator 

3.1 Python Educator Online

3.2 Create a blog

3.3 Create a youtube channel

3.4 Create eBooks

3.5 Starting a podcast 

3.6 selling a digital product (PDF)

3.7 selling an online course/ Online Pre-recorded Course

3.1 Python Educator Online

Udemy

Teachable

Skillshare

Pluralsite – requires an invite

04. Build a startup

4.1 Providing software service 

4.2 Web Development Services:

4.3 Create a Mobile App

4.4 E-commerce Solutions: 

4.1 Providing software service 

4.2 Web Development Services:

Create dynamic websites and web applications using Python frameworks like Django or Flask. Offer your services to businesses or individuals looking to establish an online presence.

4.3 Create a Mobile App

Develop desktop applications or mobile apps using Python frameworks like PyQt or Kivy. You can create tools, utilities, or even games for different platforms

4.4 E-commerce Solutions: 

Build custom e-commerce platforms using Python and frameworks like Django. You can help businesses set up online stores and manage their inventory, payments, and orders.

05. Consulting: 

Become a Python programming consultant, advising businesses on best practices, architecture, and solutions to their technical challenges.

06. Open Source Contributions: 

Contribute to open-source Python projects and gain recognition within the developer community. This can lead to paid opportunities, sponsorship, or consulting requests.

Remember that building a reputation and a network within your chosen field is essential for success. Be sure to continuously improve your skills, showcase your work through a portfolio, and stay up-to-date with the latest trends and technologies in Python programming.

07.  Passive Income 

7.1 becoming an affiliate marketer (Amazon)

7.2 becoming an affiliate marketer (google adsense)

7.3 starting a business and automation (Domain & Hosting)

7.4 Book Writing (World Tour, Skilled Based, English)

08. Win coding contests

Backend development using Python involves creating the server-side logic and infrastructure that power web applications, APIs, and other services. Python offers a variety of tools, frameworks, and libraries to streamline backend development. 

Here’s a step-by-step guide to getting started with backend development using Python:

Choose a Backend Framework:
Select a backend web framework to build your application. Some popular choices include:

Django: A high-level framework that follows the “batteries-included” philosophy, offering a complete solution for building robust web applications with features like authentication, database handling, and more.
Flask: A micro-framework that provides flexibility and simplicity, allowing you to choose and integrate the components you need for your application.

Setting Up the Development Environment:

Install Python and a code editor/IDE (Integrated Development Environment) of your choice. Consider using virtual environments to manage project-specific dependencies and isolate your project from the global Python environment.

Define Application Structure:

Plan the structure of your backend application, including organizing directories for templates, static files, models, views, and other components.

Create Models:

In Django, define your data models using Django’s Object-Relational Mapping (ORM) system. In Flask, you can use an Object-Relational Mapping (ORM) library like SQLAlchemy.

Implement Views and Controllers:

Define routes and views (in Django) or routes and controllers (in Flask) to handle requests from clients. These views/controllers process data, perform logic, and interact with models.

Implement Authentication and Authorization:

Secure your backend by implementing user authentication and authorization mechanisms to control access to resources and endpoints.

Handle Forms and Input:

Implement form handling and data validation using libraries and tools provided by your chosen framework.

Connect to Databases:

Integrate your application with a database using the ORM provided by your chosen framework. Popular choices include PostgreSQL, MySQL, SQLite, and MongoDB.

Testing:

Write unit tests, integration tests, and functional tests to ensure the reliability and functionality of your backend code. Use testing frameworks like unittest (built-in), pytest, or nose2.

API Development (Optional):

If your application requires a RESTful API, design and implement API endpoints using tools and libraries like Django REST framework (for Django) or Flask-RESTful (for Flask).

Middleware and Extensions:

Utilize middleware and extensions provided by your framework to add functionality like caching, error handling, logging, and more.

Deployment:

Deploy your backend application to a web server or a cloud platform. Popular options include using services like AWS, Heroku, or DigitalOcean.

Monitoring and Scaling:

Monitor the performance of your backend application and scale resources as needed to handle increased traffic.

Remember that backend development involves both coding skills and an understanding of web architecture and best practices. Be prepared to work with databases, APIs, security measures, and various aspects of web development. It’s also important to keep your code well-structured, maintainable, and documented to ensure the success and longevity of your backend project.

Computer graphics using Python involves creating visual content, images, and animations using programming. There are several libraries and frameworks available that make it possible to generate graphics and visualizations. 

Here are some key tools and concepts related to computer graphics using Python:

Matplotlib:

Matplotlib is a popular 2D plotting library that allows you to create a wide range of static, interactive, and animated visualizations, including line plots, bar charts, scatter plots, histograms, and more.

Seaborn:

Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical visualizations, making it easier to visualize complex datasets.

Plotly:

Plotly is a versatile library that supports interactive visualizations and can create interactive plots, charts, and dashboards suitable for web applications.

Pillow (PIL Fork):

Pillow is a library for working with images in various formats. It can be used for basic image manipulation tasks, such as resizing, cropping, filtering, and more.

OpenCV:

OpenCV (Open Source Computer Vision Library) is widely used for computer vision tasks and image processing. It provides tools for real-time computer vision, object detection, face recognition, and more.

OpenGL with PyOpenGL:

PyOpenGL is a Python wrapper for the OpenGL (Open Graphics Library) API, allowing you to create 3D graphics and interactive applications. It’s useful for creating computer games, simulations, and visualizations.

Turtle Graphics:

The turtle module in Python is a great starting point for learning computer graphics concepts. It’s a simple way to create drawings and visual patterns by controlling a “turtle” that moves on the screen.

Computer Animation:

Libraries like Manim and Pygame can be used for creating animations and simulations. Manim is specifically designed for mathematical animations, while Pygame offers a broader range of game development capabilities.

3D Graphics with Blender and BlenderScript:

Blender is a powerful 3D graphics software, and you can use Python (BlenderScript) to automate tasks, create animations, and generate scenes programmatically.

Geospatial Visualization:

Libraries like GeoPandas and Folium are used for visualizing geospatial data, such as maps, spatial data analysis, and interactive web maps.

Generative Art:

Python can be used for generative art, where algorithms are used to create visual art dynamically. Libraries like Processing.py and NodeBox can aid in this creative process.

Scientific Visualization:

Libraries like Mayavi and Vispy allow you to create 3D scientific visualizations for fields like physics, astronomy, and engineering.

When delving into computer graphics using Python, consider your specific goals and the type of visual content you want to create. Depending on your project, you might choose one or more of these libraries and approaches to achieve the desired results.

Networking development using Python is a powerful way to create applications that interact with networks, whether it’s for building web applications, working with APIs, implementing network protocols, or managing network infrastructure. 

Here are some key aspects of networking development using Python:

Socket Programming:

Python’s built-in socket library allows you to create networking applications at a lower level, enabling communication between devices over various protocols like TCP and UDP.

HTTP Requests and APIs:

Libraries like requests provide a simple and elegant way to make HTTP requests and interact with APIs. You can retrieve data from web servers, send POST requests, and handle responses.

Web Frameworks:

Python has powerful web frameworks like Flask and Django that enable you to build web applications, RESTful APIs, and other networked services with ease.

Web Scraping:

You can use libraries like BeautifulSoup and Scrapy to extract data from websites and perform web scraping tasks, which is useful for data gathering and analysis.

Networking Libraries:

Python offers networking libraries like twisted that provide higher-level abstractions for building network servers, clients, and protocols.

Networking Protocols:

Python is well-suited for implementing custom network protocols. You can create servers and clients for protocols like FTP, SMTP, POP3, IMAP, and more.

Network Analysis and Packet Manipulation:

Libraries like scapy allow you to analyze, manipulate, and craft network packets, making them useful for network security, testing, and debugging.

Network Automation and Management:

Python can be used for automating network tasks such as configuring routers, switches, and other network devices using libraries like Netmiko or NAPALM.

Network Security and Penetration Testing:

Python is employed in network security tasks like penetration testing and vulnerability assessment. Libraries like Nmap and pynacl are used for various security-related tasks.

WebSocket and Real-Time Applications:

For real-time applications and interactive features, you can use Python libraries like websockets to implement WebSocket communication.

DNS Manipulation:

Libraries like dnspython allow you to interact with DNS servers, perform domain lookups, and manage DNS records programmatically.

Network Visualization:

Tools like NetworkX can be used to create, analyze, and visualize complex networks and graphs.

When working on networking development with Python, keep in mind the importance of error handling, security considerations, and best practices. Also, make sure to refer to relevant documentation and resources to effectively use the libraries and tools available for your specific networking tasks.

Mobile app development using Python is possible through various frameworks and tools that allow you to create cross-platform applications for both iOS and Android platforms. 

Here are a few options:

Kivy:

Kivy is an open-source Python library for developing multitouch applications. It’s particularly well-suited for creating touch-based mobile applications. Kivy supports various input methods, and its code can be used to target different platforms.

BeeWare:

BeeWare is a collection of tools for developing Python applications that run on different platforms, including mobile devices. Toga is a part of BeeWare that allows you to create native applications for iOS, Android, and desktop platforms using the same codebase.

PyQt and PySide:

PyQt and PySide are Python bindings for the Qt application framework. While primarily used for desktop applications, these libraries can also be utilized to develop mobile applications. Note that using these libraries for mobile development might require some additional effort.

KivyMD:

KivyMD is a Python library that builds upon Kivy to provide Material Design components for your mobile applications. It’s suitable for creating visually appealing and functional cross-platform mobile apps.

SL4A (Scripting Layer for Android):

SL4A allows you to use various scripting languages, including Python, to interact with Android’s API. While not a traditional mobile development framework, it’s useful for creating scripts that can control Android devices.

PyJNIus:

PyJNIus is a Python library that allows you to access Java classes and methods from Python. It can be used to build Android applications using Python while leveraging Java APIs.

Chaquopy:

Chaquopy is a plugin that allows you to include Python code in your Android applications. This can be useful when you want to combine Python with Java/Kotlin code in an Android project.

It’s important to note that while these frameworks and tools enable you to use Python for mobile app development, they may have limitations compared to native development in terms of performance and access to platform-specific features. However, they can be a great choice for cross-platform development or when you want to leverage your existing Python skills for mobile development.

Before choosing a framework, evaluate your project’s requirements, target platforms, and the level of access to native features you need. Additionally, keep an eye on the maintenance and community support of the chosen framework to ensure its longevity.

Software testing is a critical process in software development to ensure that the software functions as intended and meets quality standards. Python can be used for various aspects of software testing, including unit testing, integration testing, and automation testing.

Here’s a step-by-step guide to software testing using Python:

Understand Software Testing:

Familiarize yourself with the concepts of software testing, types of testing (unit, integration, functional, etc.), and the importance of testing in the development lifecycle.

Choose a Testing Framework:

Python has several testing frameworks that facilitate different types of testing:

unittest: Python’s built-in testing framework, inspired by Java’s JUnit.
pytest: A popular, feature-rich testing framework that simplifies testing code.
nose2: A successor to the ‘nose’ testing framework, used for test discovery and execution.

Setup Your Environment:

Install the chosen testing framework using pip.

Set up a project structure that includes directories for tests, source code, and any additional assets.

Writing Unit Tests:

Unit tests focus on individual components or functions of your software:

Write test cases to cover various scenarios, including edge cases and normal behavior.

Use assertions to check that your code produces the expected output.

Integration Testing:

Integration tests ensure that different components of your software work together:

Test the interactions between components, modules, or services.

Simulate real-world scenarios to identify issues related to data flow and communication.

Automation Testing:

Automate the execution of tests to save time and ensure consistency:

Use testing frameworks to automate the execution of test cases.

Integrate tests into your development workflow, running them automatically upon code changes.

Test Fixtures and Setup:

Use fixtures and setup functions to prepare the environment for testing:

Setup functions run before and after each test case to create a clean state.

Fixtures provide reusable resources or data for tests.

Running Tests:

Use the testing framework’s command-line tools to run tests.

Leverage test discovery to automatically locate and execute test cases.

Generating Test Reports:

Many testing frameworks generate test reports for better visualization:

Understand the test reports and use them to identify failing tests and track progress.

Continuous Integration (CI):

Integrate testing into your CI/CD pipeline for automatic testing during code integration:

Use CI services like Jenkins, Travis CI, or CircleCI to automate test execution.

Code Coverage Analysis:

Measure code coverage to identify areas of your code that are not covered by tests:

Tools like coverage help you determine how much of your codebase is exercised by your tests.

Fixing and Maintaining Tests:

Regularly review and update tests to keep up with code changes and maintain test reliability.

Best Practices:

Follow best practices for writing effective tests:

Write tests before writing code (Test-Driven Development, TDD).

Keep tests independent, isolated, and atomic.

Use descriptive test case names.

Test both expected behavior and edge cases.

Community and Resources:

Participate in testing communities, read relevant articles, and explore tutorials to enhance your testing skills.

Remember, software testing is an ongoing process that requires continuous improvement and adaptation. Properly tested software is more reliable and has fewer issues when deployed to production.