country_code string | country string | station_count int64 | port_count int64 | fast_station_share float64 | fast_port_share float64 |
|---|---|---|---|---|---|
AD | Andorra | 96 | 259 | 0.0625 | 0.138996 |
AE | United Arab Emirates | 131 | 346 | 0.175573 | 0.410405 |
AF | Afghanistan | 1 | 1 | 0 | 0 |
AL | Albania | 15 | 16 | 0.6 | 0.5625 |
AM | Armenia | 4 | 6 | 0.25 | 0.166667 |
AR | Argentina | 22 | 36 | 0.954545 | 0.972222 |
AT | Austria | 1,282 | 3,474 | 0.176287 | 0.35118 |
AU | Australia | 1,241 | 2,701 | 0.533441 | 0.593114 |
AX | Aland Islands | 3 | 5 | 0.666667 | 0.8 |
AZ | Azerbaijan | 2 | 2 | 0.5 | 0.5 |
BA | Bosnia And Herzegovina | 39 | 52 | 0.153846 | 0.115385 |
BB | Barbados | 1 | 2 | 0 | 0 |
BE | Belgium | 1,245 | 2,849 | 0.128514 | 0.241488 |
BG | Bulgaria | 59 | 84 | 0.508475 | 0.595238 |
BH | Bahrain | 1 | 1 | 0 | 0 |
BR | Brazil | 644 | 922 | 0.402174 | 0.516269 |
BW | Botswana | 1 | 2 | 0 | 0 |
BY | Belarus | 40 | 68 | 0.825 | 0.852941 |
CA | Canada | 16,490 | 20,197 | 0.199212 | 0.270634 |
CH | Switzerland | 878 | 1,998 | 0.215262 | 0.396396 |
CL | Chile | 171 | 186 | 0.409357 | 0.387097 |
CN | China | 12 | 28 | 0.25 | 0.642857 |
CO | Colombia | 61 | 133 | 0.52459 | 0.413534 |
CR | Costa Rica | 171 | 230 | 0.362573 | 0.421739 |
CY | Cyprus | 91 | 157 | 0.142857 | 0.171975 |
CZ | Czech Republic | 555 | 803 | 0.29009 | 0.336239 |
DE | Germany | 23,373 | 46,401 | 0.139434 | 0.215082 |
DK | Denmark | 2,178 | 6,965 | 0.133609 | 0.266762 |
DO | Dominican Republic | 368 | 375 | 0.192935 | 0.197333 |
EC | Ecuador | 21 | 54 | 0 | 0 |
EE | Estonia | 169 | 210 | 0.923077 | 0.895238 |
EG | Egypt | 457 | 1,190 | 0.231947 | 0.255462 |
ES | Spain | 17,825 | 53,762 | 0.354783 | 0.382184 |
ET | Ethiopia | 1 | 1 | 0 | 0 |
FI | Finland | 1,873 | 7,165 | 0.194875 | 0.216748 |
FO | Faroe Islands | 5 | 9 | 0.8 | 0.888889 |
FR | France | 13,820 | 21,932 | 0.12974 | 0.385783 |
GB | United Kingdom | 26,825 | 50,100 | 0.165443 | 0.236627 |
GE | Georgia | 50 | 68 | 0.18 | 0.161765 |
GG | Guernsey | 15 | 30 | 0 | 0 |
GH | Ghana | 3 | 7 | 0 | 0 |
GI | Gibraltar | 7 | 36 | 0.142857 | 0.055556 |
GR | Greece | 277 | 469 | 0.314079 | 0.469083 |
GT | Guatemala | 1 | 1 | 0 | 0 |
HK | Hong Kong | 223 | 1,116 | 0.165919 | 0.176523 |
HR | Croatia | 267 | 482 | 0.535581 | 0.593361 |
HU | Hungary | 864 | 1,847 | 0.224537 | 0.226854 |
ID | Indonesia | 412 | 502 | 0.456311 | 0.48008 |
IE | Ireland | 2,002 | 7,125 | 0.240759 | 0.236211 |
IL | Israel | 295 | 701 | 0.935593 | 0.928673 |
IM | Isle Of Man | 41 | 113 | 0.04878 | 0.026549 |
IN | India | 1,188 | 2,065 | 0.377946 | 0.431961 |
IQ | Iraq | 2 | 2 | 0.5 | 0.5 |
IS | Iceland | 432 | 1,279 | 0.303241 | 0.268178 |
IT | Italy | 10,354 | 22,305 | 0.220977 | 0.288187 |
JE | Jersey | 29 | 85 | 0.206897 | 0.141176 |
JM | Jamaica | 33 | 62 | 0.393939 | 0.419355 |
JO | Jordan | 88 | 177 | 0.431818 | 0.621469 |
JP | Japan | 1,641 | 2,158 | 0.195612 | 0.378128 |
KE | Kenya | 12 | 15 | 0 | 0 |
KG | Kyrgyzstan | 1 | 1 | 0 | 0 |
KH | Cambodia | 22 | 36 | 0.681818 | 0.75 |
KR | Korea, Republic Of | 161 | 1,098 | 1 | 1 |
KZ | Kazakhstan | 3 | 11 | 0.666667 | 0.909091 |
LI | Liechtenstein | 8 | 20 | 0.125 | 0.5 |
LK | Sri Lanka | 58 | 79 | 0.137931 | 0.101266 |
LT | Lithuania | 960 | 993 | 0.415625 | 0.41994 |
LU | Luxembourg | 88 | 188 | 0.056818 | 0.196809 |
LV | Latvia | 83 | 169 | 0.939759 | 0.934911 |
MA | Morocco | 151 | 258 | 0.443709 | 0.492248 |
MC | Monaco | 37 | 37 | 0.027027 | 0.027027 |
MD | Moldova, Republic Of | 34 | 36 | 0.676471 | 0.638889 |
ME | Montenegro | 32 | 58 | 0.1875 | 0.137931 |
MK | Macedonia | 12 | 19 | 0.25 | 0.263158 |
MM | Myanmar | 1 | 1 | 1 | 1 |
MO | Macao | 2 | 8 | 1 | 1 |
MT | Malta | 55 | 56 | 0 | 0 |
MX | Mexico | 579 | 1,365 | 0.069085 | 0.144322 |
MY | Malaysia | 611 | 985 | 0.297872 | 0.411168 |
NAM | Namibia | 1 | 1 | 0 | 0 |
NL | Netherlands | 8,091 | 12,299 | 0.043752 | 0.143995 |
NO | Norway | 4,790 | 29,697 | 0.293946 | 0.380813 |
NP | Nepal | 1 | 1 | 0 | 0 |
NZ | New Zealand | 978 | 2,245 | 0.365031 | 0.374165 |
OM | Oman | 22 | 47 | 0.863636 | 0.87234 |
PA | Panama | 6 | 6 | 0.333333 | 0.333333 |
PE | Peru | 7 | 9 | 0 | 0 |
PH | Philippines | 16 | 25 | 0.4375 | 0.56 |
PK | Pakistan | 3 | 4 | 0 | 0 |
PL | Poland | 461 | 921 | 0.368764 | 0.410423 |
PR | Puerto Rico | 4 | 10 | 0.25 | 0.1 |
PS | Palestinian Territory, Occupied | 3 | 41 | 0.333333 | 0.02439 |
PT | Portugal | 3,696 | 7,765 | 0.473485 | 0.520927 |
PY | Paraguay | 47 | 73 | 0.893617 | 0.876712 |
QA | Qatar | 4 | 18 | 1 | 1 |
RE | Reunion | 8 | 18 | 0.375 | 0.277778 |
RO | Romania | 715 | 1,291 | 0.39021 | 0.55151 |
RS | Serbia | 109 | 196 | 0.440367 | 0.505102 |
RU | Russian Federation | 2,203 | 2,606 | 0.699955 | 0.676132 |
RW | Rwanda | 2 | 8 | 1 | 1 |
End of preview. Expand
in Data Studio
π 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
π¦ Whatβs inside (files)
All CSVs live under data/:
data/charging_station.csvβ charging stations (main table)data/charging_station_ml.csvβ ML-oriented derived table (compact / engineered signals)data/country_summary.csvβ per-country rollup (counts + fast-share)data/world_summary.csvβ extended rollup (counts + power stats + fast/ultra flags)data/ev_models.csvβ EV model specs (make/model/variant + metadata)
Additional repo files:
OCM_CC_BY_4.0.txtβ Open Charge Map attribution textCHANGELOG.md,LICENSE
π§© 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>").
π Quick start
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.
π Data dictionary
charging_station.csv (stations table)
Typical columns include:
id,namecity,state_province,country_codelatitude,longitudeports,power_kwpower_class,is_fast_dc
country_summary.csv (country rollup)
Columns:
country_code,countrystation_count,port_countfast_station_share,fast_port_share
world_summary.csv (extended rollup)
Columns (includes country summary + extra indicators):
country_code,countrystation_count,port_countfast_station_count,fast_port_countfast_station_share,fast_port_sharemax_power_kw,median_power_kwdc_fast_station_count,dc_ultra_station_counthas_fast_dc,has_ultra_dc
ev_models.csv (EV models)
Columns:
make,model,variantpowertrain,segment,body_stylefirst_year,origin_country,market_regions
π― Suggested uses
- Compare charging coverage across countries/regions
- Fast-DC vs slow infrastructure analysis
- Geospatial dashboards & planning
- Enrich infra analytics with EV model metadata
π License & attribution
- Charging station data: Contains data Β© Open Charge Map contributors (CC BY 4.0)
- Dataset packaging: CC BY 4.0 β attribution required
- Downloads last month
- 188
Size of downloaded dataset files:
29.3 MB
Size of the auto-converted Parquet files:
14.9 MB
Number of rows:
301,429