This project demonstrates data cleaning, preprocessing, and interactive visualization using Python, Pandas, and Dash. It focuses on Italy's economic and development indicators, sourced from the World Bank's World Development Indicators dataset. The cleaned data is used to generate an interactive dashboard for insights.
Below is a preview of the interactive dashboard built using Dash.
📂 Italy-Data-Cleaning-Dashboard
│-- 📂 data/
│ ├── ORIGINAL_DATA.zip (Raw dataset from World Bank)
│ ├── ITA_Data.csv (Italy’s uncleaned data)
│ ├── ITA_Data_Clean.csv (Cleaned dataset)
│-- 📂 scripts/
│ ├── Data_Cleaning.ipynb (Jupyter Notebook for data cleaning)
│ ├── Data_Cleaning.py (Python script for automated cleaning)
│-- 📂 dashboard/
│ ├── ITA_Dashboard.py (Dash-powered interactive visualization)
│ ├── assets/ (Images and styles for dashboard)
│-- README.md (Project documentation)
│-- requirements.txt (Dependencies for running the project)
✅ Data extraction and cleaning using Pandas & Python
✅ Handling missing values, duplicates, and outliers
✅ Interactive dashboard using Dash & Plotly
✅ Well-structured project with automation for reproducibility
git clone https://github.com/atchrispinto/Italy-Data-Cleaning-Visualization-using-Python-and-Dash.git
cd Italy-Data-Cleaning-Visualization-using-Python-and-Dashpip install -r requirements.txtpython scripts/Data_Cleaning.pypython dashboard/ITA_Dashboard.py- Python (Pandas, NumPy, Plotly, Dash)
- Jupyter Notebook for data exploration
- Dash Framework for visualization
- Git & GitHub for version control
🔹 Expand the dashboard with more visualization options
🔹 Include neighbor country comparisons (France, Switzerland)
🔹 Optimize performance for large datasets
Chris Pinto