costinflation/american-cheese-prices-raw-dataset-2026
12,039 raw U.S. American cheese price observations across 12 ZIP markets and 29 days. American Cheese Prices Raw Dataset (2026) Analyze 12,039 unaggregated product-level listed retail prices for American cheese slices 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… See the full description on the dataset page: https://huggingface.co/datasets/costinflation/american-cheese-prices-raw-dataset-2026.
12,039 raw U.S. American cheese price observations across 12 ZIP markets and 29 days.
American Cheese Prices Raw Dataset (2026)
Analyze 12,039 unaggregated product-level listed retail prices for American cheese slices 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
The P90 comparable price was 3.4× the P10. Across compatible rows, the 0.375 pounds-equivalent price ranged from a P10 of $2.73 to a P90 of $9.19, a 3.4× spread. This is a distribution description, not a matched-product quality or value claim.
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 0.375 pounds. Compatible mass units normalize to 0.375 pounds, equivalent to the fixture's modeled eight 0.75-ounce slices. This is a mass basis and does not assume every observed slice has the same weight. Original package fields remain available for audit.
Columns
Project ideas
- Compare mass-normalized American-cheese prices
- Track matched product titles through time
- Run reproducible EDA and missing-value analysis
Quick start with Pandas
import pandas as pd
df = pd.read_csv(
"costinflation-american-cheese-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 83 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.
- 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). American Cheese Prices Raw Dataset (2026). CC0 1.0 Universal.
Resources
Released by CostInflation Team.