Skip to content

README.md

Latest commit

 

History

History
79 lines (55 loc) · 1.86 KB

README.md

File metadata and controls

79 lines (55 loc) · 1.86 KB

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.

🧰 Tech Stack

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

📁 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

📌 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.

❓ 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

📚 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

📎 Folder Structure

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