Automation_Python_script

📊 Monthly Revenue SQL Analysis (2024)

This project demonstrates an end-to-end data analytics workflow using Google Colab and Supabase. The goal is to analyze monthly revenue data (Jan–Dec 2024) stored in Excel/CSV format and answer real-world business questions using SQL.

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🧰 Tech Stack

• 🐍 Python (Google Colab)
• 🧮 Pandas
• 📁 CSV/Excel
• 🌐 Supabase (PostgreSQL)
• 📝 Supabase SQL Editor

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📁 Dataset

12 monthly CSV files named in the format:
monthly_revenue_plan - Jan_2024.csv, ..., monthly_revenue_plan - Dec_2024.csv

Columns:

• date
• city_code
• plans
• plan_revenue_crores

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📌 Project Workflow

1. Data Upload
   Uploaded all 12 monthly files to Google Colab using files.upload().

2. Data Processing

   • Read all CSVs into Pandas DataFrames
   • Concatenated into one final DataFrame
   • Cleaned and validated the data

3. Supabase Connection

   • Used psycopg2 to connect to Supabase PostgreSQL instance
   • Created a table and uploaded the cleaned data

4. SQL Queries & Analysis
   All queries were written and executed using the Supabase SQL Editor.

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❓ Business Questions Solved

1. Total Revenue Calculation
2. Revenue Tracking by City and Date
3. Top Revenue-Generating Plan
4. Retrieve City Codes Using a Specific Plan
5. Total Revenue Contribution for Plan P3

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📚 Key Learnings

• Cloud-based SQL workflow with Supabase
• Writing ad-hoc SQL queries for business insights
• Automating Excel import and upload to PostgreSQL
• Handling real datasets in Python

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📎 Folder Structure

.
├── notebooks/
│   └── monthly_revenue_analysis.ipynb  # Google Colab notebook
├── data/
│   └── monthly_revenue_plan - Jan_2024.csv
│   └── ...
├── README.md