🐍 Python Learning Roadmap: From Basics to Advanced (Beginner → Pro)

Organized in logical progression — each topic builds on the previous one. Includes what to learn, why it matters, and real-world use cases.

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🟢 Level 1: Python Basics (Beginner)

Goal: Write simple scripts and understand syntax.

1. Getting Started

• Installing Python (via Anaconda or official installer)
• Running Python: IDLE, Jupyter, VS Code, terminal
• print(), input(), comments

💡 Use Case: Hello World, user interaction

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2. Variables & Data Types

• Variables (no declaration needed)
• Basic types: int, float, str, bool, None
• Type checking: type()
• Type conversion: int("5"), str(3.14)

💡 Use Case: Storing user input, calculations

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3. Operators

• Arithmetic: +, -, *, /, //, %, **
• Comparison: ==, !=, >, <
• Logical: and, or, not
• Assignment: =, +=, -=

💡 Use Case: Conditions, math operations

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4. Control Flow

• if, elif, else
• Nested conditions
• Ternary operator: x = "even" if num % 2 == 0 else "odd"

💡 Use Case: Decision-making in programs

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5. Loops

• for loop: for i in range(5):
• while loop: while x < 10:
• break, continue, pass
• Looping through strings, lists

💡 Use Case: Processing lists, repeating tasks

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6. Strings

• String creation, quotes (', ", ''')
• Indexing & slicing: s[0], s[1:4]
• Methods: .upper(), .lower(), .split(), .join(), .strip()
• f-strings: f"Hello {name}!"

💡 Use Case: Text processing, formatting output

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7. Data Structures

🚀 This topic has been expanded into four detailed notebooks!

• 7a. Lists []: The most common, general-purpose, and mutable collection.
• 7b. Tuples (): Immutable collections, used for fixed data.
• 7c. Sets {}: For storing unique items and performing mathematical set operations.
• 7d. Dictionaries {}: For storing data as key-value pairs.

📌 Master: list comprehensions, dictionary methods, set operations, and when to use each type.

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🟡 Level 2: Functions & Modularity (Intermediate)

8. Functions

• Defining: def greet(name):
• Parameters & arguments
• return vs print
• Default arguments: def func(x=5):
• Keyword arguments: func(name="Ali", age=25)

💡 Use Case: Reusable code blocks

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9. Scope & Namespaces

• Local vs Global variables
• global keyword
• LEGB rule (Local → Enclosing → Global → Built-in)

⚠️ Avoid global variables when possible

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10. Built-in Functions & Modules

• Common functions: len(), sum(), min(), max(), sorted(), enumerate(), zip()
• Importing modules: import math, from datetime import datetime
• Standard library: os, sys, json, random

💡 Use Case: File handling, date/time, randomness

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11. File Handling

• Reading/writing text files: open("file.txt", "r")
• Context manager: with open(...) as f:
• Working with JSON: json.load(), json.dump()

💡 Use Case: Save/load data, config files

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12. Error Handling

• try, except, else, finally
• Handling specific errors: ValueError, FileNotFoundError
• Raising errors: raise ValueError("Invalid input")

💡 Use Case: Make programs robust

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🟠 Level 3: Object-Oriented Programming (OOP) (Intermediate → Advanced)

13. Classes & Objects

• class Person:, __init__() constructor
• Creating objects: p = Person("Ali")
• Instance variables & methods
• self explained

💡 Use Case: Modeling real-world entities (User, Car, Document)

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14. OOP Principles

Concept	Explanation	Example
Encapsulation	Hide data using _private or __very_private	self.__salary
Inheritance	Child class inherits from parent	class Student(Person):
Polymorphism	Same method, different behavior	student.walk() vs teacher.walk()
Abstraction	Hide complex logic	Use methods without knowing internals

💡 Use Case: Code reuse, clean architecture

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15. Special (Dunder) Methods

• __str__() → str(obj)
• __repr__() → debug representation
• __len__() → len(obj)
• __add__() → obj1 + obj2

💡 Make your classes behave like built-in types

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16. Properties & Getters/Setters

• Use @property to control access
• Avoid direct attribute access

@property
def age(self):
    return self._age

💡 Use Case: Validation, computed properties

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🔴 Level 4: Advanced Python (Advanced)

17. Modules & Packages

• Creating your own modules (mymodule.py)
• __init__.py for packages
• Installing packages: pip install requests

💡 Use Case: Organize large projects

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18. Functional Programming

• First-class functions: pass functions as arguments
• lambda: square = lambda x: x**2
• map(), filter(), reduce()
• List/dict/set comprehensions (advanced)

💡 Use Case: Data transformation, clean code

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19. Iterators & Generators

• iter(), next()
• yield vs return
• Memory-efficient: def fib(): yield ...

💡 Use Case: Large datasets, streaming data

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20. Decorators

• @decorator syntax
• Create your own: timing, logging
• Accept arguments: @retry(attempts=3)

def timer(func):
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        print(f"Time: {time.time()-start}s")
        return result
    return wrapper

💡 Use Case: Logging, caching, authentication

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21. Context Managers

• with open() as f:
• Create your own: __enter__, __exit__
• @contextmanager decorator

💡 Use Case: Safe resource handling (files, DB connections)

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22. Exception Customization

• Define custom exceptions:

  class InsufficientFundsError(Exception):
      pass

💡 Use Case: Domain-specific errors

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23. Working with Libraries

• requests: HTTP calls
• json, csv: Data formats
• os, shutil: File system
• datetime: Date/time
• collections: defaultdict, Counter, namedtuple

💡 Use Case: Real-world scripting and automation

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24. Packaging a Project

• Project structure (pyproject.toml, src/)
• Building (build package)
• Uploading (twine package)

💡 Use Case: Distribute your code for others to pip install

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25. Virtual Environments & pip

• python -m venv myenv
• source myenv/bin/activate (Linux/Mac) or myenv\Scripts\activate (Windows)
• pip install, pip freeze > requirements.txt

💡 Use Case: Isolate project dependencies

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🔮 Level 5: Next Steps (After Mastery)

26. Popular Libraries to Explore

Library	Purpose
numpy	Numerical computing
pandas	Data analysis
matplotlib/seaborn	Data visualization
requests	Web APIs
flask/fastapi	Web development
pytest	Testing
jupyter	Interactive notebooks

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27. Best Practices

• PEP 8 (code style)
• Docstrings: """Explain function here"""
• Type hints: def func(x: int) -> str:
• Writing clean, readable code
• Unit testing with unittest or pytest

🚀 A detailed notebook for this topic, 27_Best_Practices.ipynb, is available in the 05_Next_Steps folder!

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28. Project Ideas to Practice

1. To-Do List App (CLI) → Use lists, files
2. Contact Book → Use dictionaries, JSON
3. Calculator → Functions, error handling
4. Web Scraper → requests, BeautifulSoup
5. Blog System → Classes, file handling
6. Quiz Game → OOP, files, loops

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📚 Learning Resources

Resource	Link
Official Python Docs	https://docs.python.org
Real Python (Tutorials)	https://realpython.com
Automate the Boring Stuff	https://automatetheboringstuff.com
Corey Schafer (YouTube)	https://youtube.com/c/Coreyms
W3Schools Python	https://w3schools.com/python
LeetCode / HackerRank	Practice coding problems

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🗺️ Learning Plan (8-Week Suggestion)

Week	Focus
1-2	Basics: variables, loops, strings, lists
3	Functions, file handling, error handling
4	Dictionaries, sets, comprehensions
5	OOP: classes, inheritance, methods
6	Advanced: decorators, generators, context managers
7	Projects & libraries (requests, json, os)
8	Virtual envs, best practices, type hints