A record, in full
US grocery prices by area (USDA F-MAP, historical)
Schema, a real sample, a live query box, snippets, and the machine-readable license.
US grocery prices by area (USDA F-MAP, historical)
source USDA ERS — Food-at-Home Monthly Area Prices ·
license U.S. Government public domain ·
retrieved 2018-12-01
redistribution unrestricted, commercial use permitted, no attribution required
redistribution unrestricted, commercial use permitted, no attribution required
Schema
| column | type | null % |
|---|---|---|
| id | integer | 0.0 |
| year | integer | 0.0 |
| month | integer | 0.0 |
| efpg_code | integer | 0.0 |
| efpg_name | text | 0.0 |
| tier1_group | text | 0.0 |
| tier2_group | text | 0.0 |
| region_code | integer | 0.0 |
| region_name | text | 0.0 |
| purchase_dollars_wtd | real | 0.0 |
| purchase_grams_wtd | real | 0.0 |
| purchase_dollars_unwtd | real | 0.0 |
| purchase_grams_unwtd | real | 0.0 |
| number_stores | real | 0.0 |
| unit_value_mean_wtd | real | 0.0 |
| unit_value_se_wtd | real | 0.0 |
| unit_value_mean_unwtd | real | 0.0 |
| price_index_geks | real | 0.0 |
Sample · 5 rows
| id | year | month | efpg_code | efpg_name | tier1_group | tier2_group | region_code | region_name | purchase_dollars_wtd | purchase_grams_wtd | purchase_dollars_unwtd | purchase_grams_unwtd | number_stores | unit_value_mean_wtd | unit_value_se_wtd | unit_value_mean_unwtd | price_index_geks |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113400 | 2018 | 12 | 78000 | Infant formula | Other foods | Infant formula | 37980 | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 7242991.843 | 273925920.8 | 4701417.722 | 185089805.8 | 957 | 2.644142556 | 0.056816754 | 2.540073831 | 1.017085438 |
| 113316 | 2018 | 12 | 78000 | Infant formula | Other foods | Infant formula | 35620 | New York-Newark-Jersey City, NY-NJ-PA | 25241988.77 | 1029012848 | 10121100.13 | 489473678.1 | 2100 | 2.453029507 | 0.143643042 | 2.067751665 | 1.075809791 |
| 113232 | 2018 | 12 | 78000 | Infant formula | Other foods | Infant formula | 33100 | Miami-Fort Lauderdale-West Palm Beach, FL | 7979975.565 | 287353864.9 | 6731990.51 | 250105851.2 | 855 | 2.777055241 | 0.038766143 | 2.691656544 | 1.021283249 |
| 113148 | 2018 | 12 | 78000 | Infant formula | Other foods | Infant formula | 31080 | Los Angeles-Long Beach-Anaheim, CA | 11933047.46 | 621819186.8 | 8193391.252 | 402096511.3 | 1372 | 1.919054239 | 0.048921142 | 2.037667829 | 1.033998362 |
| 113064 | 2018 | 12 | 78000 | Infant formula | Other foods | Infant formula | 26420 | Houston-The Woodlands-Sugar Land, TX | 10431731.45 | 404208210.3 | 6007905.031 | 243965143.3 | 711 | 2.580781683 | 0.049863772 | 2.462607958 | 1.020259945 |
Query it — live
Use it
# every response carries x-license curl "https://api.datafloe.dev/v1/datafloe/grocery-prices?year=2018" \ -H "Authorization: Bearer datafloe_live_…"
const r = await fetch( "https://api.datafloe.dev/v1/datafloe/grocery-prices?year=2018", { headers: { Authorization: "Bearer datafloe_live_…" } } ); const { data, meta } = await r.json();
import httpx r = httpx.get( "https://api.datafloe.dev/v1/datafloe/grocery-prices", params={"year": "2018"}, headers={"Authorization": "Bearer datafloe_live_…"}, ) data = r.json()["data"]
License · machine-readable
{
"license_id": "us-public-domain@1",
"spdx_analog": null,
"rights": {
"commercial_use": true,
"redistribution": true,
"raw_download": true,
"ai_training": false
},
"attribution_required": false,
"attribution_text": "U.S. Department of Agriculture, Economic Research Service. (2024). Food-at-Home Monthly Area Prices.",
"provenance": {
"uploader": "datafloe",
"declared_origin": "licensed",
"source_url": "https://www.ers.usda.gov/data-products/food-at-home-monthly-area-prices",
"version": "2018-12",
"content_hash": "sha256:27f9450b747e8f1f63821165ae31c3746a0c35afc11caaf66e7c2df15c485940",
"published_at": "2018-12-01"
},
"price": {
"model": "free",
"monthly_usd": 0
},
"terms_url": "https://www.usa.gov/government-works"
} ai_training is false on every dataset by design — you learn we're license-explicit before training rights are ever sold.