---
pretty_name: "Global EV Charging Stations & EV Models (2025)"
license: cc-by-4.0
language:
  - en
size_categories:
  - 100K<n<1M
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - electric-vehicles
  - ev-charging
  - charging-stations
  - mobility
  - geospatial
  - global
  - tabular
  - dataset
  - eda

configs:
  - config_name: stations
    data_files:
      - split: train
        path: "data/charging_station.csv"

  - config_name: stations_ml
    data_files:
      - split: train
        path: "data/charging_station_ml.csv"

  - config_name: country_summary
    data_files:
      - split: train
        path: "data/country_summary.csv"

  - config_name: world_summary
    data_files:
      - split: train
        path: "data/world_summary.csv"

  - config_name: ev_models
    data_files:
      - split: train
        path: "data/ev_models.csv"
---

# 🌍 Global EV Charging Stations & EV Models (2025)

**Author:** Tarek Masryo  
**License:** CC BY 4.0  
**Version:** v1.0 (2025-09-15)

A clean, analysis-ready snapshot of global EV infrastructure:
- Main stations table: **242,417 rows** (charging sites)
- Companion summaries: country + world rollups
- EV models table for enrichment

---

## 📦 What’s inside (files)

All CSVs live under `data/`:

- `data/charging_station.csv` — charging stations (main table)
- `data/charging_station_ml.csv` — ML-oriented derived table (compact / engineered signals)
- `data/country_summary.csv` — per-country rollup (counts + fast-share)
- `data/world_summary.csv` — extended rollup (counts + power stats + fast/ultra flags)
- `data/ev_models.csv` — EV model specs (make/model/variant + metadata)

Additional repo files:
- `OCM_CC_BY_4.0.txt` — Open Charge Map attribution text
- `CHANGELOG.md`, `LICENSE`

---

## 🧩 Why configs?

This repo includes multiple CSVs with different schemas.  
Configs make the Hub viewer stable and let you load each table explicitly via `load_dataset(repo_id, "<config>")`.

---

## 🚀 Quick start

```python
from datasets import load_dataset, get_dataset_config_names

repo_id = "tarekmasryo/global-ev-infra-dataset"
print(get_dataset_config_names(repo_id))

# Stations
stations = load_dataset(repo_id, "stations")["train"].to_pandas()

# Summaries
country = load_dataset(repo_id, "country_summary")["train"].to_pandas()
world   = load_dataset(repo_id, "world_summary")["train"].to_pandas()

# EV models
models  = load_dataset(repo_id, "ev_models")["train"].to_pandas()

print(stations.shape, country.shape, world.shape, models.shape)
```

> Tip: `load_dataset(repo_id)` will load the first config (`stations`) if you omit the config name.

---

## 📚 Data dictionary

### `charging_station.csv` (stations table)
Typical columns include:
- `id`, `name`
- `city`, `state_province`, `country_code`
- `latitude`, `longitude`
- `ports`, `power_kw`
- `power_class`, `is_fast_dc`

### `country_summary.csv` (country rollup)
Columns:
- `country_code`, `country`
- `station_count`, `port_count`
- `fast_station_share`, `fast_port_share`

### `world_summary.csv` (extended rollup)
Columns (includes country summary + extra indicators):
- `country_code`, `country`
- `station_count`, `port_count`
- `fast_station_count`, `fast_port_count`
- `fast_station_share`, `fast_port_share`
- `max_power_kw`, `median_power_kw`
- `dc_fast_station_count`, `dc_ultra_station_count`
- `has_fast_dc`, `has_ultra_dc`

### `ev_models.csv` (EV models)
Columns:
- `make`, `model`, `variant`
- `powertrain`, `segment`, `body_style`
- `first_year`, `origin_country`, `market_regions`

---

## 🎯 Suggested uses

- Compare charging coverage across countries/regions
- Fast-DC vs slow infrastructure analysis
- Geospatial dashboards & planning
- Enrich infra analytics with EV model metadata

---

## 📜 License & attribution

- Charging station data: **Contains data © Open Charge Map contributors** (CC BY 4.0)
- Dataset packaging: CC BY 4.0 — attribution required