costinflation/hamburger-bun-prices-raw-dataset-2026
24,551 raw U.S. hamburger bun price observations across 12 ZIP markets and 29 days. Hamburger Bun Prices Raw Dataset (2026) Analyze 24,551 unaggregated product-level listed retail prices for hamburger buns across 12 U.S. ZIP markets from July 13 through August 10, 2026. The single analysis-ready CSV preserves titles, dates, geography, package quantities, listed prices, and a source-neutral comparable-price field. What “raw” means here: unaggregated product-level observations… See the full description on the dataset page: https://huggingface.co/datasets/costinflation/hamburger-bun-prices-raw-dataset-2026.
24,551 raw U.S. hamburger bun price observations across 12 ZIP markets and 29 days.
Hamburger Bun Prices Raw Dataset (2026)
Analyze 24,551 unaggregated product-level listed retail prices for hamburger buns across 12 U.S. ZIP markets from July 13 through August 10, 2026. The single analysis-ready CSV preserves titles, dates, geography, package quantities, listed prices, and a source-neutral comparable-price field.
What “raw” means here: unaggregated product-level observations after scope and safety filtering. The file includes derived normalization fields; it is not untouched acquisition data.
Dataset at a glance
A result worth investigating
59.7% of rows expose a quantity-cleaning challenge. Only 9,900 of 24,551 rows (40.3%) have a compatible resolved bun count for the 10 buns comparison. The unresolved rows remain in the file with their listed prices and a blank comparable-price field, making missingness an explicit analysis surface.
What one row represents
One row is one quality-filtered product-title, ZIP-market, date, package-quantity, and listed-price observation. It is not a sale, order, demand, inventory, market-share, or consumer record. Distinct offers may share a title, ZIP, and date; exact duplicate public rows are removed.
Comparable-price method
The fixed comparison target is 10 buns. Compatible count units normalize to 10 buns; unresolved pack counts remain blank. Original package fields remain available for audit.
Columns
Project ideas
- Analyze ten-bun package-equivalent prices
- Track matched product titles through time
- Compare selected ZIP markets
- Build a reproducible retail-price dashboard
- Run reproducible EDA and missing-value analysis
Quick start with Pandas
import pandas as pd
df = pd.read_csv(
"costinflation-hamburger-bun-retail-prices-raw-2026-07-13-to-2026-08-10.csv",
dtype={"geography_id": "string"},
)
comparable = df.dropna(subset=["normalized_price_amount"]).copy()
print(comparable["normalized_price_amount"].describe())Quality and limitations
- All 348 expected date × ZIP-market combinations are present.
- Exact duplicate public rows: 0. The preparation pass removed 37 byte-identical input repeats.
- Scope review excluded 0 definite out-of-scope or unsafe titles representing 0 input rows.
- Product titles are descriptive text, not stable public product identifiers.
- ZIP labels describe selected markets, not citywide estimates; the panel is not nationally representative.
- Availability and title wording can change unmatched aggregates through assortment change.
- Shipping, tax, redeemed promotions, purchases, and product performance are outside the dataset.
License
Released under CC0 1.0 Universal for unrestricted reuse. Attribution is not required, but citation helps others find the release.
CostInflation Team. (2026). Hamburger Bun Prices Raw Dataset (2026). CC0 1.0 Universal.
Resources
Released by CostInflation Team.