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🧼 DataWash

A Flask web app that cleans and analyzes CSV files — upload messy data, get instant insights.

Python Flask Pandas License


✨ Features

  • Upload any CSV file. (up to 16MB)
  • Preview raw data with instant stats.
  • Auto-clean: nulls, duplicates, whitespace, data types.
  • Analysis dashboard - shape, dtypes, missing values, summary stats.
  • Download the cleaned CSV.

🛠 Tech Stack

Layer Tech
Backend Python 3.11, Flask
Data Pandas, NumPy
Frontend HTML5, CSS3, Bootstrap 5
Deploy Render

🚀 Local Setup

# 1. Clone the repo
git clone https://github.com/laypatel13/datawash.git
cd datawash

# 2. Create and activate virtual environment
python -m venv venv
source venv/bin/activate        # Mac/Linux
# venv\Scripts\activate         # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the app
python app.py

Visit http://localhost:5000 in your browser.


📁 Project Structure

DataWash/
├── app.py                # Flask entry point
├── requirements.txt      # Dependencies
├── .gitignore            # Version control ignore rules
├── uploads/              # User-uploaded CSV files
├── templates/            # HTML templates
│   ├── base.html         # Base layout
│   ├── index.html        # Upload + preview page
│   └── results.html      # Analysis dashboard
├── static/
│   ├── css/              # Stylesheets
│   └── js/               # JavaScript (none yet)
└── utils/                # Helper modules
    ├── cleaning.py       # Data cleaning logic
    ├── analysis.py       # Data analysis logic
    └── storage.py        # File upload/download

🌐 Deployment

Deployed on Render — see render.yaml for configuration.

Note: Hosted on Render free tier — first load may take 20–30s to wake up.

Live URL: https://data-wash-sr98.onrender.com


📄 License

MIT License © 2026 Lay Patel

About

A Flask web app that automatically cleans and analyzes CSV files. Upload any messy dataset and get instant insights — missing values, duplicates, data types, and interactive charts. Built with Python, Flask, Pandas, and Plotly. Deployed live on Render.

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