The Question
After building a personal fiber tracker, I got curious about the economics of fiber intake. Which vegetables give you the most fiber bang for your buck?
Eating enough fiber can be expensive if you're not strategic about it. I wanted to know: what's the most cost-effective way to hit daily fiber goals? Which vegetables offer the best price-per-gram of fiber?
The Experiment
Stage 1: Finding the Data Sources
What I Tried:
I looked for public datasets that combined nutritional information with pricing data. I found two key sources:
What I Learned:
- USDA Food Data Central - Nutritional information including fiber content per 100g
- Bureau of Labor Statistics - Average retail food prices by region
Stage 2: Data Matching Challenge
What I Tried:
I attempted to automatically match food items between the nutritional database and pricing database to calculate price per gram of fiber.
What I Learned:
Food names aren't standardized between datasets. "Brussels sprouts" vs "Brussels sprouts, fresh" vs "Sprouts, Brussels" required manual mapping.
This was the hardest part - creating a mapping system that could handle naming variations across both datasets.
Stage 3: Analysis and Insights
What I Tried:
Built a Python script to pull and clean the datasets, match food items, calculate price per gram of fiber, and generate a ranked list.
What I Learned:
Some surprising insights emerged:
Best Value Fiber Sources:
- Navy beans - $0.02 per gram of fiber
- Split peas - $0.03 per gram of fiber
- Lentils - $0.04 per gram of fiber
- Brussels sprouts - $0.12 per gram of fiber
Expensive Fiber:
- Artichokes - $0.45 per gram of fiber
- Asparagus - $0.38 per gram of fiber
- Avocados - $0.32 per gram of fiber
Final Output
This analysis changed how I shop. I now buy more:
- Dried legumes (incredible fiber value)
- Frozen Brussels sprouts (cheaper than fresh, same fiber)
- Seasonal vegetables when prices drop
And less:
- Out-of-season "superfood" vegetables
- Pre-packaged salad mixes (terrible fiber-to-price ratio)
Technical Details
Tech Stack:
- Python
- Pandas for data manipulation
- Requests library for API calls
Data Sources:
- USDA Food Data Central API
- Bureau of Labor Statistics - Average Price Data
The Python script performs these key steps:
- Pull and clean the datasets
- Match food items between nutritional and price data
- Calculate price per gram of fiber
- Generate a ranked list
Try It Yourself
Planning to expand this to include:
- Regional price variations
- Seasonal price tracking
- Other nutrients (protein, vitamins)
- Organic vs conventional comparisons
Sometimes the most interesting projects come from combining everyday problems with available data. Who knew grocery shopping could be this analytical?