Python Road Map

Table of Contents

Phase 1: Introduction to Python

Python is an object-oriented, interpreter programming language that comes with standard libraries freely available in source or binary form for all major platforms from

The Python Language Reference

This reference manual describes the syntax and “core semantics” of the language. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming.

The Python Standard Library

While The Python Language Reference describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. It also describes some of the optional components that are commonly included in Python distributions.

Advance – Extension Options

The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications. To write extensions in C or C++, read Extending and Embedding the Python Interpreter and Python/C API Reference Manual. There are also several books covering Python in depth.


Python is an object-oriented programming language that reads/interprets code line by line. When you install Python, it downloads the standard libraries into your machine (written in C), which provides foundation and access to system functionalities such as file I/O.

Python Installation and Setup

The Python interpreter is usually installed as /usr/local/bin/

On Unix-based systems, by typing python in the shell, the default system python that comes with the operating system is invoked. To specify another python version, type in python3 for all python 3 versions, unless the environment variable is set to link python to a different version.

An Informal Introduction to Python

Escape characters

Escaping characters in programming languages like Python is essential for several reasons:

  • Representing Special Characters: Some characters have special meanings in strings, such as newline (\n), tab (\t), or backslash (\\). Escaping allows you to include these special characters in a string without invoking their special functions.

General Syntax: In most programming languages, escape sequences start with a backslash (\). Here’s a list of some common escape sequences:

  • \’ : Single quote
  • \” : Double quote
  • \\ : Backslash
  • \n : Newline
  • \t : Tab
  • \r : Carriage return
  • \b : Backspace
  • \f : Form feed
  • \u or \U : Unicode character

If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote:

>>> print('C:\some\name')  # here \n means newline!
>>> print(r'C:\some\name')  # note the r before the quote


The interpreter acts as a simple calculator: you can type an expression at it, and it will write the value. Expression syntax is straightforward: the operators +, -, * and / can be used to perform arithmetic; parentheses (()) can be used for grouping.

Division (/) always returns a float. To do floor division and get an integer result you can use the // operator; to calculate the remainder you can use %. With Python, it is possible to use the ** operator to calculate powers.

The equal sign (=) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt:

>>> tax = 12.5 / 100
>>> price = 100.50
>>> price * tax
>>> price + _
>>> round(_, 2)

In addition to int and float, Python supports other types of numbers, such as Decimal and Fraction. Python also has built-in support for complex numbers, and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j).


Python can manipulate text (represented by type str, so-called “strings”) as well as numbers. This includes characters “!”, words “rabbit”, names “Paris”, sentences “Got your back.”, etc. “Yay! :)”. They can be enclosed in single quotes (‘…’) or double quotes (“…”) with the same result.

The values of literals are not computed; they are taken “literally” as they are written. For example, 42 is always the integer 42, and ‘Hello’ is always the string “Hello”.

Multi-line string literals are enclosed in triple single or double quotes.

Usage: thingy [OPTIONS]
     -h                        Display this usage message
     -H hostname               Hostname to connect to

Strings can be concatenated (glued together) with the + operator, and repeated with *:

>>> # 3 times 'un', followed by 'ium’.
>>> 3 * 'un' + 'ium'
>>> 'Py' 'thon'

Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one:

word = 'Python'  # p:0, y:1, t:2, h:3, o:4, n:5
print(word[2:5])  # 'tho'

Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced.

The built-in function len() returns the length of a string:

print(len('Hello, world!'))

Phase 2: Intermediate Python


  • File Handling (reading, writing files)
  • Error and Exception Handling
  • Modules and Packages
  • Working with Libraries (e.g., os, sys, math)
  • Regular Expressions
  • List Comprehensions
  • Lambda Functions
  • Decorators
  • Generators


  • Real Python
  • Python Crash Course by Eric Matthes
  • Python Intermediate Course on Coursera


  • Reading and writing CSV files
  • Implementing a custom exception
  • Using decorators for logging


  • Build a file organizer that sorts files into folders based on file type
  • Develop a contact book application that stores and retrieves contacts from a file

Phase 3: Advanced Python


  • Object-Oriented Programming (OOP)
  • Advanced Data Structures (Queues, Stacks, Linked Lists, Trees, Graphs)
  • Working with Databases (SQLite, PostgreSQL)
  • Web Scraping (BeautifulSoup, Scrapy)
  • APIs and Web Services
  • Concurrency (Threading, Multiprocessing)
  • Testing (unittest, pytest)
  • Advanced Libraries (NumPy, Pandas, Matplotlib)


  • Fluent Python by Luciano Ramalho
  • Effective Python by Brett Slatkin
  • Advanced Python Programming on Udemy


  • Creating classes and inheritance models
  • Implementing a stack and a queue
  • Scraping data from a website


  • Develop a web scraper that collects data from multiple websites and stores it in a database
  • Create a financial dashboard using data from an API (e.g., stock prices)

Phase 4: Mastery in Python


  • Design Patterns
  • Metaprogramming
  • Performance Optimization
  • Asynchronous Programming (asyncio)
  • Machine Learning Basics (Scikit-learn, TensorFlow)
  • Data Science and Visualization (Pandas, Matplotlib, Seaborn)
  • Web Development Frameworks (Django, Flask)
  • DevOps Basics (Docker, Kubernetes)


  • Design Patterns in Python
  • Python Machine Learning by Sebastian Raschka
  • Two Scoops of Django


  • Implementing a Singleton pattern
  • Using asyncio for asynchronous tasks
  • Creating a basic machine learning model


  • Develop a full-fledged web application using Django or Flask
  • Create a machine learning model to predict housing prices
  • Build a Dockerized application and deploy it using Kubernetes

Best Approach to Learning

  1. Structured Learning:
    • Follow a structured course or a book to build a solid foundation.
    • Engage in interactive coding platforms like LeetCode, HackerRank, or CodeWars.
  2. Hands-On Practice:
    • Code along with tutorials and immediately apply what you’ve learned by solving problems and building projects.
    • Contribute to open-source projects on GitHub.
  3. Consistent Practice:
    • Dedicate regular, focused time each day to coding.
    • Participate in coding challenges and hackathons to test your skills.
  4. Deep Dive into Projects:
    • Work on progressively challenging projects.
    • Refactor and optimize your code, applying best practices and design patterns.
  5. Community Engagement:
    • Join Python communities (e.g., Reddit’s r/learnpython, Stack Overflow).
    • Attend local meetups or online webinars and workshops.
  6. Continuous Learning:
    • Stay updated with the latest Python developments through blogs, podcasts, and news.
    • Keep exploring advanced topics and new libraries as they emerge.

By following this roadmap and approach, you can progressively build your skills from a beginner to an advanced Python programmer. Happy coding!

Step-by-Step Guide to Contributing to Open-Source Projects on GitHub

    1. Set Up GitHub Account: If you don’t have a GitHub account, create one at GitHub.
    2. Explore Open-Source Projects: Browse repositories on GitHub to find projects that interest you. Use the search bar or explore trending repositories. Look for projects with tags like good first issue, beginner, or help wanted to find beginner-friendly issues.
    3. Understand the Project: Read the project’s README file to understand its purpose, how to set it up, and how to contribute. Check the file, if available, for specific contribution guidelines.
    4. Fork the Repository: Fork the repository to create a copy in your GitHub account. This allows you to make changes without affecting the original project.
    5. Clone the Repository: Clone the forked repository to your local machine using the following command:
git clone
    1. Set Up the Project: Follow the setup instructions in the README file to install dependencies and run the project locally.
    2. Create a Branch: Create a new branch for your changes. Naming it after the feature or fix you’re working on helps keep things organized:
git checkout -b branch-name
    1. Make Changes: Implement the feature or fix the issue on your local branch. Ensure your code adheres to the project’s coding standards and guidelines.
    2. Commit Your Changes: Commit your changes with a clear and descriptive message:
git add .
git commit -m "Description of the changes"
    1. Push to GitHub: Push your changes to your forked repository on GitHub:
git push origin branch-name
  1. Create a Pull Request: Navigate to the original repository on GitHub. Click the Compare & pull request button. Provide a clear description of your changes and submit the pull request.
  2. Engage with the Community: Respond to any feedback or requests for changes from the project maintainers. Be open to constructive criticism and willing to make adjustments as needed.
  3. Merge and Celebrate: Once your pull request is reviewed and approved, it will be merged into the main codebase. Celebrate your contribution and look for more opportunities to help!

Visual Guide

To visually understand the process, you can refer to various online resources and diagrams. Here is a simple flowchart to visualize the steps:

| Set Up GitHub Account  |
| Explore Projects       |
| Understand the Project |
| Fork the Repository    |
| Clone the Repository   |
| Set Up the Project     |
| Create a Branch        |
| Make Changes           |
| Commit Your Changes    |
| Push to GitHub         |
| Create a Pull Request  |
| Engage with Community  |
| Merge and Celebrate    |

Additional Tips

  • Join Open-Source Communities: Participate in forums and chat groups like Discord, Slack, or Reddit dedicated to open-source projects.
  • Attend Hackathons: These events often involve contributing to open-source projects and can provide hands-on experience and mentorship.
  • Follow Open-Source Guides: Websites like First Timers Only and Up For Grabs list beginner-friendly projects and issues.


Text Sequence Type — str: Strings are examples of sequence types, and support the common operations supported by such types. String Methods: Strings support a large number of methods for basic transformations and searching.


String literals that have embedded expressions. A formatted string literal or f-string is a string literal that is prefixed with ‘f’ or ‘F’. These strings may contain replacement fields, which are expressions delimited by curly braces {}. While other string literals always have a constant value, formatted strings are really expressions evaluated at run time.

Example 1: Simple Replacement Field

name = "Alice"
print(f"Hello, {name}")

{name}: The field_name is name, which is replaced with the value of the variable name. Output: Hello, Alice

Example 2: Accessing an Attribute

class User:
    def __init__(self, name): = name

user = User("Bob")
print(f"User's name is {}")

{}: The field_name is, which accesses the name attribute of the user object. Output: User’s name is Bob

Example 3: Accessing an Element

user = {"name": "Charlie"}
print(f"User's name is {user['name']}")

{user[‘name’]}: The field_name is user[‘name’], which accesses the element with key name in the user dictionary. Output: User’s name is Charlie

Example 4: Using a Conversion

value = 123.456
print(f"The value is {value!r}")

{value!r}: The conversion !r uses repr(), so the value is displayed as 123.456 (with quotes if it were a string). Output: The value is 123.456

Example 5: Using a Format Specifier

value = 123.456
print(f"The value is {value:.2f}")

{value:.2f}: The format_spec :.2f formats the value to 2 decimal places. Output: The value is 123.46

Example 6: Combining Conversion and Format Specifier

value = 123.456
print(f"The value is {value!s:.1f}")

{value!s:.1f}: The conversion !s uses str(), and the format_spec :.1f formats the value to 1 decimal place. Output: The value is 123.5

Example 7: Using Equal Sign for Debugging

value = 42
print(f"value = {value=}")

{value=}: Shows both the expression and its value. Output: value = 42

Example 8: Nested Replacement Fields

value = 42
print(f"{{The value is {value}}}")

{{The value is {value}}}: Double curly braces {{ and }} are replaced by single { and }, and {value} is replaced by 42. Output: {The value is 42}

Example 9: Complex Field Name with Attributes and Elements

class User:
    def __init__(self, name, details): = name
        self.details = details

user = User("Dave", {"age": 30})
print(f"User's name is {} and age is {user.details['age']}")

{}: Accesses the name attribute. {user.details[‘age’]}: Accesses the details dictionary’s age key. Output: User’s name is Dave and age is 30


These examples cover a variety of f-string capabilities, including:

  • Simple variable replacement.
  • Accessing attributes and elements.
  • Using conversions and format specifiers.
  • Combining multiple features.