pyfooda 0.3.0

Python API for USDA FoodData Central with LLM-powered food aggregation

Pyfooda

A compact Python API for USDA FoodData Central with an LLM-powered pipeline that compresses ~296k raw food items into a clean everyday nutrition database.

Repository structure

pyfooda/                       # Installable Python package (end-user API)
  api.py                       #   lookup functions
  data/
    fooddata.csv               #   296k preprocessed USDA foods
    nutrients.csv              #   nutrient metadata + daily reference values

scripts/                       # Data pipeline (not part of the package)
  build_fooddata.py            #   Step 1 — raw USDA CSV → fooddata.csv
  aggregate.py                 #   Step 2 — fooddata.csv → foods_aggregated.json
  aggregator.py                #   aggregation engine (used by aggregate.py)
  aggregation_prompt.txt       #   tweakable LLM prompt
  nutrients_drv.py             #   nutrient definitions + DRVs
  requirements.txt             #   pipeline dependencies

---

1 · Install the package

pip install pyfooda          # from PyPI
# or
pip install -e .             # editable install from source

The package ships with the preprocessed USDA data — no downloads needed.

2 · Use the lookup API

import pyfooda as pf

pf.find_closest_matches("cheddar")           # up to 10 partial-name matches
pf.get_nutrients("Cheddar Cheese")           # dict of nutrient values
pf.get_category("Cheddar Cheese")            # "Cheese"
pf.get_portion_gram_weight("Cheddar Cheese") # grams per portion
pf.get_portion_unit_name("Cheddar Cheese")   # e.g. "cup, shredded"

df  = pf.get_fooddata_df()   # full 296k × 44 DataFrame
drv = pf.get_drv_df()        # daily reference values per nutrient

Function	Returns
get_category(name)	Food category (str)
get_nutrients(name)	dict[nutrient → value] or None
get_portion_gram_weight(name)	float or None
get_portion_unit_name(name)	str or None
find_closest_matches(partial)	list[str] (max 10)
get_fooddata_df()	Full food DataFrame
get_drv_df()	Nutrient DRV DataFrame

---

3 · Data pipeline (for contributors / rebuilding)

The scripts/ directory contains the full pipeline that produces the data shipped with the package. You only need this if you want to rebuild from a newer USDA release or re-run the aggregation.

Prerequisites

pip install -r scripts/requirements.txt

Step 1 — Build fooddata.csv from raw USDA download

1. Download the CSV bundle from FoodData Central
2. Extract it (e.g. ~/Downloads/FoodData_Central_csv_2024-10-31/)
3. Run:

python scripts/build_fooddata.py ~/Downloads/FoodData_Central_csv_2024-10-31

This reads the raw USDA tables (food.csv, food_nutrient.csv, food_category.csv, etc.), joins and pivots them, and writes the result to pyfooda/data/fooddata.csv + pyfooda/data/nutrients.csv.

Step 2 — Aggregate into a compact everyday database

The raw database has 295,943 items — dozens of entries for "cheddar cheese" alone. The aggregator uses an LLM to classify each food as:

Action	Meaning
CREATE	Start a new generic food (e.g. "Cheddar Cheese")
ADD	Merge into an existing generic (nutrients averaged)
IGNORE	Skip (supplements, additives, unidentifiable)

The LLM sees each food's name, category, nutrient profile, and the closest existing entries, so it makes nutritionally-informed decisions (e.g. "Tonic Water" ≠ "Lime Juice").

export OPENROUTER_API_KEY="sk-or-..."

# Quick test — first 1000 items (~20 API calls, ~2 min)
python scripts/aggregate.py test

# Full run — all 296k items
python scripts/aggregate.py full

# Resume after interruption
python scripts/aggregate.py full --resume

Output:

File	Description
pyfooda/data/foods_aggregated.json	Generic name, averaged nutrients, source USDA IDs
pyfooda/data/foods_aggregated.csv	Flat CSV for quick inspection

Tweaking the aggregation

Edit scripts/aggregation_prompt.txt to change how the LLM classifies foods. For example you could add:

• "Merge all yogurt flavors into a single Yogurt entry"
• "Keep organic and conventional separate"
• "Ignore all baby food"

License

MIT