⚡ EV Charging Analytics Dashboard

Production-style Streamlit dashboard for exploring global EV charging infrastructure with Plotly and PyDeck.

It is designed as a decision-facing analytics tool: infrastructure KPIs, geographic coverage, fast-DC mix, inequality metrics, opportunity scoring, and allocation scenarios.

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📌 Overview

This dashboard explores the Global EV Charging Stations Dataset:

• 🌍 Charging stations across multiple countries
• 🎛️ Filters for country, city, power class, fast-DC status, and port range
• 📊 KPIs for stations, ports, average power, and fast-DC share
• 📈 Distribution analytics including Lorenz curve and Gini concentration
• 🗺️ Interactive world map with port-scaled station markers
• 🧮 Allocation optimizer for fast-DC expansion scenarios
• 🧭 Compare mode for selected slices vs global or preset views

Dataset repository:

https://github.com/tarekmasryo/Global-EV-Charging-Stations

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🖼️ Screenshots

Overview

Map

Insights

Optimizer

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🔑 Key Features

• Country, city, power-class, fast-DC, and ports filters
• Executive KPIs for charging infrastructure monitoring
• Charging mix analysis by country and power class
• Port concentration analysis with Pareto view, Lorenz curve, and Gini score
• Impact and opportunity scoring for expansion prioritization
• Interactive PyDeck map with station-level markers
• Rule-based optimizer for allocating new fast-DC capacity
• Export-ready tables for country summaries and scenario outputs

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🧱 Project Structure

.
├── EV-Charging-Analytics.py
├── src/ev_charging_dashboard/
│   ├── analytics.py
│   ├── data.py
│   ├── services.py
│   └── __init__.py
├── tests/
├── assets/
├── requirements.txt
├── requirements-dev.txt
├── Makefile
└── pyproject.toml

The Streamlit entrypoint is intentionally named:

EV-Charging-Analytics.py

Keep this filename when deploying the dashboard.

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🚀 Run Locally

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
python -m streamlit run EV-Charging-Analytics.py

If PowerShell blocks activation, run this only for the current shell:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

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⚙️ Data Configuration

The app auto-loads the dataset from the public GitHub raw CSV by default.

You can override the data source in any of the following ways, listed by priority:

1. Query parameter: ?csv=<path_or_url>
2. Streamlit secrets file: .streamlit/secrets.toml

DATA_URL = "https://raw.githubusercontent.com/tarekmasryo/Global-EV-Charging-Stations/main/data/charging_station.csv"

3. Environment variables: CSV_URL, CSV_PATH, or DATA_URL
4. Manual CSV upload from the sidebar

Optional enrichment files:

• world_population.csv
• country_region.csv

When these files are missing, enrichment metrics are skipped gracefully.

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☁️ Deploy on Streamlit Community Cloud

1. Push this repository to GitHub.
2. Create a new Streamlit Cloud app from the repository.
3. Set the main file path to:

EV-Charging-Analytics.py

4. Add DATA_URL in Streamlit Secrets when you want to override the default dataset URL.

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🧪 Development

Install development tools:

python -m pip install -r requirements-dev.txt
pre-commit install

Run checks:

ruff check .
ruff format --check .
pytest -q

Or use:

make lint
make format-check
make test

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📜 License & Attribution

• Code: Apache-2.0. See LICENSE.
• Dataset: hosted in the linked dataset repository.

Attribution:

Global EV Charging Dashboard and Dataset by Tarek Masryo.