costinflation/coffee-creamer-prices-raw-dataset-2026
109,184 raw U.S. coffee creamer price observations across 12 ZIP markets and 29 days. Coffee Creamer Prices Raw Dataset (2026) Analyze 109,184 unaggregated product-level listed retail prices for coffee creamers 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/coffee-creamer-prices-raw-dataset-2026.
109,184 raw U.S. coffee creamer price observations across 12 ZIP markets and 29 days.
Coffee Creamer Prices Raw Dataset (2026)
Analyze 109,184 unaggregated product-level listed retail prices for coffee creamers 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
80.1% of rows expose a quantity-cleaning challenge. Only 21,694 of 109,184 rows (19.9%) have a compatible resolved liquid-creamer volume for the 32 fluid ounces 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 32 fluid ounces. Only compatible liquid-volume packages normalize to 32 fluid ounces. Powder, count-based, and unresolved packages remain as listed-price rows with a blank comparable price. Original package fields remain available for audit.
Columns
Project ideas
- Study liquid-versus-powder quantity missingness
- Run reproducible EDA and missing-value analysis
Quick start with Pandas
import pandas as pd
df = pd.read_csv(
"costinflation-coffee-creamer-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 686 byte-identical input repeats.
- Scope review excluded 24 definite out-of-scope or unsafe titles representing 3,381 input rows.
- Product titles are descriptive text, not stable public product identifiers.
- ZIP labels are selected markets, not citywide or national estimates.
- 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). Coffee Creamer Prices Raw Dataset (2026). CC0 1.0 Universal.
Resources
Released by CostInflation Team.