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Target

Target Locations dataset (June 2026)

2,130 locations across 2 countries and 1,486 cities, with 13 fields per row. Available in CSV and ZIP formats.

Rows
2,130
Fields
13
Countries
2
Collected
Jun 2026
Browse Target locations →

License

Licensed under CC BY 4.0. Requires attribution. License & attribution guide →

Fields

# Column Field
1 store_id Store ID
2 store_name Store 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 store_open_date Premium Store Open Date
12 opening_hours Premium Opening Hours
13 status Premium Status

Sample rows

The first 5 rows, straight out of the file.

store_id store_name country_code country city region zip_code address Premium coordinates phone_number Premium store_open_date Premium opening_hours Premium status Premium
US:1007 Laurel US United States Maryland City Maryland 20724 3343 Corridor Marketplace [39.09699,-76.80966] +1 301 483 0934 2008-07-24 Mo-Sa 07:00-23:00; Su 07:00-22:00 Open
US:1009 Sterling US United States Cascades Virginia 20164 46201 Potomac Run Plz [39.02629,-77.40061] +1 703 444 8440 2004-10-09 Mo-Su 08:00-22:00 Open
US:100 Ridgedale US United States Minnetonka Minnesota 55305 13201 Ridgedale Dr [44.97016,-93.44741] +1 952 542 8250 2007-02-10 Mo-Su 07:00-22:00 Open
US:1000 Westminster US United States Westminster Maryland 21157 200 Malcolm Dr [39.56674,-76.97427] +1 410 848 6180 2011-04-04 Mo-Su 07:00-22:00 Open
US:1010 Boulevard Mall US United States Tonawanda Town New York 14228 1575 Niagara Falls Blvd [42.99859,-78.81867] +1 716 831 8750 2008-07-24 Mo-Su 07:00-22:00 Open

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/target?download=csv")
print(df.head())
DuckDB
SELECT country, count(*) AS n
FROM read_csv('https://bizlocationdb.com/locations/target?download=csv')
GROUP BY 1 ORDER BY n DESC;