README-GoogleAPI-dataset.md

metadata

pretty_name: EV Charging Stations in Finland (Google Places API)
size_categories:
  - 1K<n<10K
tags:
  - electric-vehicles
  - ev-charging
  - finland
  - geospatial
  - google-places

EV Charging Stations in Finland (Google Places API)

This dataset lists every public EV charging station in Finland that Google Places returns. Each row is one Google place of type electric_vehicle_charging_station. For each place it gives the location and address, opening hours and Google rating. It also lists the connectors at the site, with each connector's type, maximum charging power and, for most sites, live availability.

Rows	4,958 charging locations
Columns	17
Geography	Finland, including Åland: 554 towns, 1,092 postal codes
Source	Google Places API (New)
Snapshot	Availability data is timestamped 17 Sep 2026, 06:00–07:10 UTC (09:00–10:10 Helsinki time)
Primary key	id, the Google Place ID. Unique, with no duplicate rows.
Coordinates	WGS84. lat 59.82–70.08, lng 19.86–30.94. All points are inside Finland.

Files

File	Size	Contents
ev_finland.csv	2.7 MB	Flattened table, one row per place. This README describes this file.
ev_finland_google_api.json	6.4 MB	The raw Places API records with nested fields (displayName, location, regularOpeningHours, evChargeOptions, postalAddress, …). This is the file the Hugging Face viewer shows.

In the CSV, name comes from displayName.text, and lat/lng come from location. openingHours is regularOpeningHours.weekdayDescriptions joined with ; . connectorCount and connectors come from evChargeOptions. postalCode and regionCode come from postalAddress.

Quick start

import pandas as pd, json

df = pd.read_csv("hf://datasets/data-sci-project/ev-count-google-api/ev_finland.csv",
                 dtype={"postalCode": str})        # keep leading zeros, e.g. "00580"

# One row per connector group
conn = (df.dropna(subset=["connectors"])
          .assign(connectors=lambda d: d.connectors.map(json.loads))
          .explode("connectors"))
conn = pd.concat([conn[["id", "name"]].reset_index(drop=True),
                  pd.json_normalize(conn.connectors.tolist())], axis=1)

# Highest power per site. A site counts as a fast charger if it reaches at least 50 kW.
df["max_kw"] = df.id.map(conn.groupby("id").maxChargeRateKw.max())
fast = df[df.max_kw >= 50]                          # 1,337 sites

---

Columns

Column	Type	Filled	Distinct	Description
id	str	4,958	4,958	Google Place ID (ChIJ…, 27 chars).
name	str	4,958	192	Display name. This is almost always a brand label such as "Plugit Charging Station", not the name of the site.
primaryType	str	4,958	1	Always electric_vehicle_charging_station.
primaryTypeDisplayName	str	4,958	1	Always Electric vehicle charging station.
pureServiceAreaBusiness	bool	25	1	False on 25 rows and empty elsewhere. Carries no information.
formattedAddress	str	4,958	4,670	Full address, e.g. Kauppamiehentie 1, 21710 Pargas.
postalCode	str	4,909	1,092	Finnish postal code. Empty on 49 rows.
regionCode	str	4,958	1	Always FI.
lat, lng	float	4,958	4,953	Coordinates.
rating	float	1,508	37	Average Google rating, 1.0–5.0.
userRatingCount	float	1,508	45	Number of ratings, 1–2,880.
priceLevel	float	0	—	Always empty.
priceRange	float	0	—	Always empty.
connectorCount	float	4,273	64	Total connectors at the place, 1–367. It always equals the sum of count in connectors.
connectors	JSON str	4,273	1,466	List of connector groups. See below.
openingHours	str	4,180	71	Weekly hours as text, e.g. Monday: Open 24 hours; Tuesday: ….

The connectors field

This column is a JSON list with one object per connector type and power level at the site. For example:

[{"type": "EV_CONNECTOR_TYPE_CCS_COMBO_2", "maxChargeRateKw": 250, "count": 4,
  "availableCount": 4, "outOfServiceCount": 0,
  "availabilityLastUpdateTime": "2026-09-17T07:05:00Z"},
 {"type": "EV_CONNECTOR_TYPE_TYPE_2", "maxChargeRateKw": 22, "count": 2}]

Key	Present in	Meaning
type	all 5,673 groups	Connector standard (see table below).
maxChargeRateKw	5,672 groups	Maximum power of this connector group, in kW.
count	all	Number of connectors in the group.
availableCount	5,112 groups (90%)	Connectors free at the snapshot time.
outOfServiceCount	5,112 groups	Connectors reported broken at the snapshot time.
availabilityLastUpdateTime	5,112 groups	UTC time when the availability was last updated.

---

Known issues

1. Brand names are inconsistent. Casing and suffixes vary, e.g. Virta, VIRTA, Virta Global, Liikennevirta, and korttelilataus alongside Korttelilataus. Normalise names before grouping by brand:

   brand = (df.name.str.lower()
              .str.replace(r" charging station| global|\.fi|-lataus| oy.*", "", regex=True)
              .str.strip().replace({"liikennevirta": "virta"}))
   

2. The same site sometimes appears twice. 8 rows share 4 coordinate pairs. Helsinki Airport P2 appears as two places, each with 246 connectors. Remove duplicates before summing connectors by area.
3. Some postal codes and addresses are bad. 49 rows have no postalCode. Two codes are invalid: 115610 (Lahti) and NR9 3LL (Lappeenranta). 217 addresses contain only a postal code and town. A few are garbled, e.g. unknown, -, 20520 Turku, Hanko, and an address containing "SWEDEN" for a site in Kuusisto. For location, use lat/lng.
4. Addresses use different formats. Åland addresses put the house number first and end in Åland (8, Nyhemsvägen 22150, Åland). The regex \d{5},?\s+([^,]+) pulls the town from 4,955 of the 4,958 addresses.
5. Some power values have floating-point noise. 88 connector groups have values like 13.800000190734863. Round maxChargeRateKw to 1 decimal place.
6. Opening hours need cleaning before parsing. 24 rows write all-day opening as 12:00 AM - 11:59 PM instead of Open 24 hours. 127 rows use thin spaces (U+2009) and narrow no-break spaces (U+202F) around the times.

Note

• This was merged with data from Digitraffic, and the merged data is included in another repo.