McDonald's Locations dataset (June 2026)
21,307 locations across 27 countries and 8,854 cities, with 11 fields per row. Available in CSV and ZIP formats.
- Rows
- 21,307
- Fields
- 11
- Countries
- 27
- Collected
- Jun 2026
License
Licensed under CC BY 4.0. Requires attribution. License & attribution guide →
Fields
| # | Column | Field |
|---|---|---|
| 1 | outlet_id | Outlet ID |
| 2 | outlet_name | Outlet Name |
| 3 | country_code | Country Code |
| 4 | country | Country |
| 5 | city | City |
| 6 | region | Region |
| 7 | zip_code | ZIP Code |
| 8 | address Premium | Address |
| 9 | coordinates | Coordinates |
| 10 | phone_number Premium | Phone Number |
| 11 | opening_hours Premium | Opening Hours |
Sample rows
The first 5 rows, straight out of the file.
| outlet_id | outlet_name | country_code | country | city | region | zip_code | address Premium | coordinates | phone_number Premium | opening_hours Premium |
|---|---|---|---|---|---|---|---|---|---|---|
| US:223988 | Los Ang-Century | US | United States | Los Angeles | California | 90045 | 5223 W Century Blvd | [33.94579,-118.37086] | +1 310 410 1707 | Mo-Su 00:00-24:00 |
| US:224503 | Citrus Hgts-Lich | US | United States | Citrus Heights | California | 95621 | 7850 Lichen Dr | [38.70784,-121.31242] | +1 916 721 7773 | Mo-Su 05:00-22:00 |
| US:232423 | Enfield - Scitico Plaza | US | United States | Enfield | Connecticut | 06082 | 585 Hazard Ave | [41.98598,-72.51178] | +1 860 763 4122 | Mo-Su 05:00-23:00 |
| US:223179 | Orange-Hills | US | United States | Orange | California | 92869 | 4200 E Chapman Avenue | [33.78745,-117.80859] | +1 714 538 4337 | Mo-Su 06:00-24:00 |
| US:224862 | San Diego/Delmar | US | United States | San Diego | California | 92130 | 3505 Del Mar Heights Rd | [32.95446,-117.23138] | +1 858 755 8588 | Mo-Th 05:45-22:00; Fr-Sa 05:45-23:00; Su 06:00-22:00 |
Get the full dataset (Free)
In the free version, coordinates are rounded to two decimal places (1 km precision) and premium fields are not available.
To get over these limits, check out our paid plans.
Use it directly
Only meant for experimentation. Using it directly in production pipeline is strictly prohibited.
Python
import pandas as pd
df = pd.read_csv("https://bizlocationdb.com/locations/mcdonalds?download=csv")
print(df.head())DuckDB
SELECT country, count(*) AS n
FROM read_csv('https://bizlocationdb.com/locations/mcdonalds?download=csv')
GROUP BY 1 ORDER BY n DESC;