🐍 Python Interview Roadmap – Complete Concept .

🧩 1. Basics & Core Syntax

• Python installation, running scripts, using IDLE / VS Code
• Comments (# single, ''' multi-line ''')
• Input / Output (input(), print())
• Variables and constants
• Data types: int, float, str, bool, complex
• Type conversion (int(), float(), str())
• Operators: arithmetic, comparison, logical, assignment, identity, membership
• Conditional statements: if, elif, else
• Loops: while and for
• Control statements: break, continue, pass

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🧮 2. Data Structures

• String manipulation, slicing, methods (replace, split, join)

• List: indexing, slicing, methods (append, remove, sort, copy)

• Tuple: immutable sequences

• Set: unique elements, operations (union, intersection)

• Dictionary: key-value pairs, methods (keys(), values(), items())

• Comprehensions:

  • List comprehension
  • Dict comprehension
  • Set comprehension

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🧠 3. Functions

• Defining functions with def
• Return values & scope (local, global)
• Default, positional & keyword arguments
• *args and **kwargs
• Lambda functions (anonymous functions)
• Built-in functions: map, filter, reduce, zip, enumerate
• Recursion (calling a function inside itself)

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🧰 4. Modules & Packages

• Creating and importing your own modules
• Built-in modules: math, datetime, os, random, sys
• Installing external packages using pip
• The __name__ == "__main__" concept

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🏗️ 5. File Handling

• Open, read, write, append files (open(), with statement`)
• Working with text and CSV files
• Exception handling in file I/O

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⚠️ 6. Exception Handling

• try, except, else, finally blocks
• Handling multiple exceptions
• Custom exceptions (raise ValueError("message"))

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🧱 7. Object-Oriented Programming (OOP)

• Classes and objects
• __init__ constructor
• Instance vs. class variables
• Methods vs. static methods
• Inheritance and super()
• Polymorphism (same method, different behavior)
• Encapsulation (_protected, __private)
• Abstraction using abc module
• Dunder (Magic) Methods like __str__, __len__, __add__

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🔄 8. Advanced Function Concepts

• Decorators — add behavior to functions
• Iterators (__iter__, __next__)
• Generators (yield)
• Closures — inner functions that remember state
• Higher-Order Functions — functions taking functions as arguments

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🧩 9. Collections Module

• namedtuple
• deque
• Counter
• defaultdict
• OrderedDict

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📚 10. Regular Expressions (Regex)

• import re
• Functions: match, search, findall, sub, split
• Meta characters: . ^ $ * + ? {} [] | ()

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💾 11. Working with Data

(Very useful for analytics interviews)

• CSV handling (csv module, pandas.read_csv)
• JSON (json module)
• Excel Files (with openpyxl, pandas)
• SQLite / MySQL (using sqlite3 or mysql.connector)

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⚙️ 12. Advanced Topics

• Itertools (combinations, permutations, product)
• Functools (lru_cache, reduce, partial)
• Datetime manipulations
• Logging module
• OS and sys module (file paths, environment variables)

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🤖 13. Data Analytics & Visualization

• NumPy – arrays, broadcasting, vectorized operations
• Pandas – DataFrame operations, filtering, grouping, merging
• Matplotlib / Seaborn – plotting graphs
• Statistics – mean, median, std, correlation

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☁️ 14. Intermediate / Interview-Level Concepts

• Shallow vs. Deep Copy (copy module)
• Mutable vs. Immutable types
• Virtual environments (venv)
• Multithreading & Multiprocessing (basics)
• Python Memory Model & Garbage Collection

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🧪 15. Testing & Best Practices

• Unit Testing with unittest or pytest
• Docstrings and type hints (typing module)
• Code readability (PEP 8)
• Git + GitHub integration

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🧭 16. Projects / Integration

For analytics or interview demo:

• File data summarizer (read CSV → analyze → write output)
• SQL + Pandas dashboard
• API fetch → data cleaning → visualization
• Simple Flask web app

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✅ Summary Table

Level	Category	Key Topics
🟢 Beginner	Basics	Syntax, Variables, Loops, Conditions
🟡 Intermediate	Data Structures	Lists, Dicts, Sets, Functions
🟠 Advanced	OOP, Decorators	Classes, Inheritance, Generators
🔵 Expert	Data, OOP, Modules	Pandas, Regex, Exception Handling
🟣 Job-ready	Projects	Analytics, APIs, Automation

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Would you like me to convert this into a 30-Day Python Interview Challenge (Markdown version) — 📅 daily schedule + topics + interview Q&A + mini project — so you can upload it as DAY_1.md, DAY_2.md, etc.? That would make your GitHub repo look like a complete learning journal + portfolio project.