thefoodapi/foodproducts

The Food API: Packaged Food & Beverage Products Dataset

Normalized packaged-food, beverage and supplement product data, keyed by barcode, for food-tech, nutrition apps and e-commerce.

This repository contains the free evaluation sample for release 2026.10, packaged on 20 October 2026.

The full dataset is available for sale in JSONL, CSV and SQLite.

Get the full dataset

Dataset overview

• Records: 1,000 products across 727 brands: 747 foods, 249 beverages and 4 supplements.
• Barcodes: checksum-validated GTIN-14 strings; preserve leading zeros.
• Ingredients: ingredient statements on 1,000 products and parsed ingredient trees on 954.
• Nutrition: 285 nutrition sets across 284 products, with 3,185 nutrient rows, serving context and preparation details.
• Images: 950 unique primary image files referenced by 952 products.
• Formats: JSONL with nested nutrition sets, three related CSV tables, and SQLite.

Files

File	Contents
food_data.jsonl	One product per line, including nutrition sets and nutrients.
food_data.csv	Product records; join the nutrition tables for panel data.
nutrition_sets.csv	Nutrition panels, joined by product ID.
nutrition_nutrients.csv	Nutrients, joined by product ID and nutrition set ID.
products.sqlite	Complete sample in a relational database.
images/	Primary product images, referenced by image_name.
product-aliases.jsonl	Retired IDs whose replacement is included in this sample.
SCHEMA.md	Field definitions, loading instructions and query examples.
CHANGELOG.md	Public schema changelog.
sample-scope.json	Sample size and selection information.
checksums.json	SHA-256 fingerprints of the published files.

Sample scope

The sample uses stratified selection from the 481,031-product release, proportionally following product type and field availability. It is an evaluation sample, and does not guarantee coverage of every category. Missing values mean the fact is unavailable, not that the product is free of an ingredient or allergen.

Technical schema

Public schema version 1.0.0

Use this guide to load the dataset, join nutrition records to products and interpret missing values.

Choose a format

products.sqlite contains the complete 1,000-product sample in a single SQLite database. Open it with any SQLite client or library.

food_data.jsonl contains one product per line. Each product includes a nutrition_sets array, with a nutrients array inside each set.

The CSV version uses three files: food_data.csv, nutrition_sets.csv and nutrition_nutrients.csv. They contain the same columns as the corresponding database tables. Join nutrition sets to products using product_id. Join nutrients to sets using both product_id and nutrition_set_id.

Product IDs are integers and remain stable across releases. Barcodes are text containing 14 digits. Preserve leading zeros when importing them.

Products

One row per product. Missing product facts are null; inclusion does not require an ingredient statement, a brand, nutrition or an image. Lists and objects are stored as JSON text in SQLite and CSV. In JSONL, each product contains its nutrition sets and their nutrients.

Field	Type	Required	Meaning
id	integer	yes	Permanent product id. Gaps are normal; an id is never renumbered or reused, so it is safe to key on across releases.
gtin	string	yes	The product barcode in canonical GTIN-14 form (14 digits, left-zero-padded). Checksum-validated. To look a scanned code up, pad it to 14 digits first.
name	string	no	Product title as sold, including variant and package wording ((4 pack) …, …, 16 oz).
brand	string	no	Brand name.
product_type	string	no	food, beverage or supplement.
category	string[]	no	Category path from broad to narrow, e.g. ["Food", "Snacks, Cookies & Chips", "Cookies"].
net_content	string	no	Package quantity as printed, e.g. 16 oz, 12 fl oz, 12 count.
pack_count	integer	no	Number of units in a multipack, when stated.
food_condition	string	no	Storage condition as stated, lowercased: shelf_stable, refrigerated, frozen, dry, ready-to-eat, …
food_form	string	no	Physical form as stated, lowercased, e.g. whole, powder, liquid.
flavor	string	no	Flavor as stated.
dietary_claims	string[]	no	Claims as stated on the listing or pack, e.g. Gluten-Free, Organic, Vegetarian, Natural.
organic	boolean	no	true when the product carries an organic claim; otherwise null, never false.
nutrition_sets	object[]	yes, JSONL only	Ordered nutrition panels, each with serving context and a nutrients array. Stored in separate tables in CSV and SQLite. An empty array means no nutrition set is available.
nutrition_default_set_id	string	no	ID of the selected nutrition set owned by this product; null means no selected default.
ingredients	string	no	The full ingredient statement as printed, with formatting cleaned but no words changed.
ingredients_parsed	object[]	no	The statement as a tree of components and ingredients in label order; see Ingredient tree. Null when no parsed tree is available; ingredients retains the statement when available.
sweeteners	string[]	no	Non-nutritive sweeteners and sugar alcohols named in the ingredients, as identities in order of appearance: aspartame, sucralose, acesulfame_potassium, saccharin, steviol_glycosides, monk_fruit_extract, erythritol, xylitol, sorbitol, maltitol, allulose, … Sugars, syrups and honey are not listed.
allergens_declared	string[]	no	Allergens the package declares it contains (Contains: …), as identities: milk, egg, wheat, soy, peanut, tree_nut, almond, cashew, walnut, pecan, hazelnut, pistachio, macadamia, brazil_nut, sesame, fish, shellfish, crustacean, mollusc, mustard, celery, sulfite, coconut, gluten. A declaration that names a species (sardines, tuna, cod; shrimp, crab; clams, oysters) is read as its class (fish, crustacean, mollusc); the literal word shellfish stays shellfish.
allergens_may_contain	string[]	no	Precautionary allergens, same identities: May contain … claims and facility, equipment or line notes (Made in a bakery that may also use …).
allergens_statement	string	no	The allergen wording verbatim, including facility notes.
caffeine_statement	string	no	The caffeine claim verbatim: Caffeine-Free, Decaffeinated, Naturally Caffeinated.
caffeine_amount_mg	number	no	Declared caffeine amount in milligrams, paired with an explicit caffeine_basis. Unknown amounts stay null; a caffeine-free or decaffeinated claim alone does not supply a number.
caffeine_basis	string	no	Quantity to which the caffeine amount applies, e.g. per 15 fl oz (443 mL) can. For multipacks, a per-can amount describes one can. Both caffeine fields are null when the amount or its basis is unverified.
alcoholic_beverage	boolean	yes	true when the product is an alcoholic beverage (a stated ABV of 0.5% or more, or listed as one).
alcohol_percent	number	no	Alcohol by volume as stated, e.g. 4.7. Null when not stated.
warnings	string[]	no	Package warnings verbatim: Proposition 65 notices, phenylketonuria statements, choking and heat cautions, allergen warning text.
directions	string	no	Preparation, use and safe-handling instructions as supplied.
countries_of_sale	string[]	yes	ISO 3166 country codes where the product is sold, e.g. ["US"].
additives, nutri_score, nova_group	null	no	Reserved, always null in this version.
image_name	string	no	Filename of the main product image; see Images.
label_image_names	string[]	no	Reserved; always null. This download includes primary product images only.
source_name	string[]	yes	Unique source names contributing to this product, such as ["walmart"]. Multiple sources may contribute to the same product.
updated_at	string	yes	ISO 8601 timestamp of the release in which this record's data last changed; see Updates.

Nutrition sets

Primary key (product_id, id). id is the set ID, scoped to its product. position preserves canonical array ordering; read with ORDER BY position. The deferred foreign key from (products.id, nutrition_default_set_id) to this key prevents cross-product defaults. A null default selects nothing even if alternative sets exist.

Column	Type	Meaning
product_id	integer	Owning product
id	text	Set ID, unique within the product
position	integer	Nonnegative array position, unique within product
preparation	text	as_sold, as_prepared, unknown
heading	nullable text	Printed heading
component_name	nullable text	Component or assortment member described by this panel, if stated
preparation_description	nullable text	Preparation instructions or context
serving_size	nullable text	Printed serving wording
servings_per_container	nullable number	Supported numeric count; unknown stays null
servings_per_container_text	nullable text	Original count wording
basis	text	per_serving, per_100g, per_100ml, per_container, per_declared_quantity, unknown
reference_quantity_present	0 / 1	Distinguishes null reference object from a populated one
reference_quantity_amount	nullable number	Strictly positive when known
reference_quantity_unit	nullable text	Normalized reference unit
reference_quantity_text	nullable text	Original reference wording

JSONL groups the reference quantity columns into reference_quantity, which is either null or an object with amount, unit and text. It omits product_id and position because the parent object and array order provide that information. Separate sets describe different serving sizes, preparation methods or components. A component panel is not selected as the default for the whole product.

Nutrition nutrients

Primary key (product_id, nutrition_set_id, name), composite foreign key to the owning set. position preserves canonical ordering within the set. There is one row per named nutrient; amount and percentage are separate columns in that row.

Column	Type	Meaning
product_id	integer	Owning product
nutrition_set_id	text	Owning nutrition set
position	integer	Nonnegative array position, unique within set
name	text	Nutrient name
amount	nullable number	Exact nonnegative amount; printed zero remains zero
unit	nullable text	Unit of amount or bound; null when unknown, even with a known amount
daily_value_percent	nullable number	Printed nonnegative daily value; values above 100 are valid
bound	nullable text	less_than, less_than_or_equal, or null
bound_value	nullable number	Nonnegative threshold; null if unreadable/unknown
amount_text	nullable text	Verbatim amount wording

Exact amount and bound are mutually exclusive. A threshold without an operator is invalid. Unknown threshold and unit may independently remain null. Daily-value-only rows are valid. No missing value means zero. Numeric fields must be finite. Nutrient comparisons require known compatible units, basis and quantity context; a SQL join does not make unlike servings comparable. For example, a nutrient can have 350% DV without an absolute quantity, and a fiber entry can retain a bound with an unknown threshold or unit. Caffeine claims remain separate from nutrition panels unless the panel itself contains caffeine.

Ingredient tree

ingredients_parsed is an array of component objects. Each has component, which may be null, and an ingredients array. Each ingredient has a name, a nullable max_percent for stated percentage limits, and a nullable sub_ingredients array. The order follows the ingredient statement. Use ingredients for the full text.

Images

image_name identifies the primary product photo in images/. This sample contains 950 unique image files for 952 products. Each image is smaller than 500 KB. Use the filename extension to determine the format. Label images are not included.

Loading CSV and JSONL

CSV files use UTF-8, quoted fields and a header row. Keep barcodes as text. A cell containing exactly \N means null. An empty cell means an empty string. A non-null value beginning with a backslash has an extra leading backslash; remove one after checking for the null marker. Arrays and objects are encoded as JSON text. Booleans use 0 and 1.

JSONL uses native JSON arrays, objects, booleans and nulls. Read one line at a time to avoid loading the entire file into memory.

import json

with open("food_data.jsonl", encoding="utf-8") as records:
    for line in records:
        product = json.loads(line)
        # Process one product at a time.

Querying nutrition

This query returns calories from each product's default panel when it describes one serving. Other panels may use different quantities or preparation methods, so check the context before comparing values.

SELECT p.id, p.name, s.serving_size, n.amount, n.unit
FROM products AS p
JOIN nutrition_sets AS s
  ON s.product_id = p.id AND s.id = p.nutrition_default_set_id
JOIN nutrition_nutrients AS n
  ON n.product_id = s.product_id AND n.nutrition_set_id = s.id
WHERE s.basis = 'per_serving' AND n.name = 'Calories'
  AND n.bound IS NULL AND n.amount IS NOT NULL AND n.unit = 'kcal';

import sqlite3

with sqlite3.connect("products.sqlite") as db:
    for row in db.execute("SELECT id, gtin, name FROM products LIMIT 10"):
        print(row)

Updates

updated_at records when a product's data last changed. Product IDs are never reused. Gaps in IDs are normal and do not indicate an incomplete download. When updating, replace your existing dataset with the new complete release.

product-aliases.jsonl maps retired IDs to their replacements. The database also includes a product_aliases table for these mappings and a meta table containing release information.

Checking your downloads

checksums.json records the SHA-256 fingerprint of each file. Verification is optional, but helps detect incomplete or damaged downloads. Place it beside the files and run this script to check the files you downloaded:

import hashlib
import json
from pathlib import Path

checksums = json.loads(Path("checksums.json").read_text())
for name, expected in checksums.items():
    path = Path(name)
    if not path.is_file():
        continue
    digest = hashlib.sha256()
    with path.open("rb") as stream:
        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
            digest.update(chunk)
    print(name, "OK" if digest.hexdigest() == expected else "MISMATCH")

Licensing

This sample dataset is provided under the CC BY-NC 4.0 license for non-commercial research and evaluation.

The full dataset is available for sale. Purchase the full dataset at thefoodapi.io. The free sample remains licensed under CC BY-NC 4.0.