For Data Scientists & ML Engineers · Food Distributors
Food Distributors Data for Data Scientists
Food Distributors data for data scientists: 5 datasets on one shelf. Every one delivered as API, files, or warehouse rows.
financial time series api for backtesting · alternative data for quantitative research · where to get training data for food distributors models
API, files, or your warehouse. Daily, weekly, or hourly.
Which food distributors datasets qualify for data science work?
This slice keeps the 5 food-distributors records that clear Datadory's relevance bar for data scientists and ML engineers — 3 carry top-tier relevance, and none scores below quality 5. One navigation quirk matters for link building: the three federal series are filed under financial-exchanges-data paths, while IFDA and RangeMe live under food-distributors. For the wider industry view, see the food-distributors data hub, or browse all data-scientists resources for sibling industries.
The best food distributors datasets for data scientists, ranked
Ordered by relevance to modeling work first, then Datadory quality score, then access convenience. The one-line verdict under each name is written for pipeline builders.
Delivery is pipe-delimited .dat inside ZIP archives with a .txt field dictionary, plus CSV/XLSX table pulls from data.census.gov and XLSX index files. Static release cycle, relevance 3, quality 10.
2. Annual Integrated Economic Survey (AIES) – successor to AWTS — Model US industry structure from reference-year 2023 revenues, operating expenses, inventories, payroll and e-commerce, aggregated by 6-digit NAICS, state and metro area — the annual successor to AWTS. Ships CSV, XLS, ZIP (.dat plus FIELDS dictionary) and JSON/XML through the API. Annual refresh, relevance 2, quality 9.
Arrives as XLSX, plain CSV, or a ZIP bundling machine-readable CSV with a readme. Annual refresh, relevance 2, quality 9.
5. RangeMe – B2B Food & Grocery Product Discovery — Sample structured brand and product profiles to bootstrap a CPG product graph, subject to platform terms — useful edges for supplier-to-retailer networks, not for time series.
Do any food distributors datasets expose an API?
The Annual Integrated Economic Survey serves both JSON and XML through the same API family, with CSV and XLS for batch work.
How much history do these sources cover?
Depth varies sharply by design. The Economic Census spans the 2002–2022 census waves — six quinquennial snapshots of county-level establishments, sales and payroll — and its update status is static, so no new observations arrive between waves. The AIES contributes a single reference year, 2023, but refreshes annually, so your panel thickens over time. The Food Dollar Series is the only true continuous series here: annual observations from 2007 through 2024 across 47 spending accounts. IFDA publishes monthly and RangeMe turns over daily, giving you recency rather than history. For any model needing a long panel, plan on combining census waves with the annual series rather than expecting one source to carry two decades of continuity.
Straight answers
Is there a financial time series API for backtesting on food distributor data?
Not in the strict sense — no record here is API-first, and none carries high-frequency prices. The Economic Census exposes JSON via the Census Data API and the AIES serves JSON and XML, but both are census-grain structural data, and the Food Dollar Series is annual. For backtesting, use these as slow-moving features and source price action elsewhere.
Does food distributors data work as alternative data for quantitative research?
At the sector level, yes. County-level establishment, sales and payroll panels from the Economic Census form exogenous features independent of company disclosures, the Food Dollar Series gives a 2007–2024 food-spending breakdown across 47 accounts, and IFDA's monthly updates can be parsed into a US foodservice-demand indicator.
Where can I get training data for food-demand or food-price models?
Start tabular: the Food Dollar Series' 2007–2024 annual CSV supplies price-side features, while Economic Census county panels supply supply-side structure across 2002–2022. RangeMe profiles can label products if your model needs entity-level features.
Rows before rollout
Sample rows from any shelf entry — the field dictionary and coverage notes ride along. If the shelf misses what you need, say so; sourcing requests are half our job.
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