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This repository covers advanced SQL concepts implemented using Python. It demonstrates how to interact with databases, run complex queries, perform joins, aggregations, window functions, and more using libraries like sqlite3, SQLAlchemy, or pandas. Ideal for data analysts and developers looking to integrate SQL power into Python workflows.

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mirzayasirabdullahbaig07/Advanced-SQL-In-Python

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๐Ÿ“Š Advanced SQL in Python

Welcome to the Advanced-SQL-In-Python repository!
This project is designed to help you master advanced SQL concepts using Python tools and libraries such as sqlite3, SQLAlchemy, Pandas, and Jupyter Notebooks. Itโ€™s ideal for aspiring Data Analysts, Data Engineers, and Machine Learning Engineers who want to blend the power of SQL with Python for real-world data analysis. Here We will cover all the concepts in deep. Follow All the files and become an SQL Expert


๐Ÿ“Œ Key Topics Covered

  • ๐Ÿง  SQL Basics Recap (SELECT, WHERE, JOIN)
  • ๐Ÿ” Complex JOINs (INNER, OUTER, CROSS)
  • ๐Ÿงฎ Subqueries and Nested SELECTs
  • ๐Ÿ—๏ธ CTEs (Common Table Expressions)
  • ๐Ÿงน Window Functions (ROW_NUMBER, RANK, PARTITION BY)
  • ๐Ÿ› ๏ธ Aggregate Functions with GROUP BY and HAVING
  • ๐Ÿšฆ Conditional Logic (CASE WHEN)
  • ๐Ÿ“‚ Data Manipulation (INSERT, UPDATE, DELETE via Python)
  • ๐Ÿ“ˆ SQL with Pandas and DataFrames
  • ๐Ÿ” Secure Query Execution (Avoiding SQL injection)
  • ๐Ÿ—ƒ๏ธ Connecting to SQLite / MySQL / PostgreSQL using SQLAlchemy

๐Ÿ Tools & Libraries

  • Python 3.x
  • Jupyter Notebook / VS Code
  • sqlite3 โ€“ Built-in Python library for SQLite databases
  • pandas โ€“ For data manipulation
  • SQLAlchemy โ€“ SQL toolkit and ORM
  • ipython-sql โ€“ Running SQL directly in Jupyter cells

๐Ÿš€ Getting Started

  1. Clone the Repository
    git clone https://github.com/mirzayasirabdullahbaig07/Advanced-SQL-In-Python.git
    cd Advanced-SQL-In-Python
    

๐Ÿ”— Connect With Me


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This repository covers advanced SQL concepts implemented using Python. It demonstrates how to interact with databases, run complex queries, perform joins, aggregations, window functions, and more using libraries like sqlite3, SQLAlchemy, or pandas. Ideal for data analysts and developers looking to integrate SQL power into Python workflows.

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