costinflation/feminine-wipe-prices-raw-dataset-2026
17,893 raw U.S. feminine wipe price observations across 12 ZIP markets and 29 days. Feminine Wipe Prices Raw Dataset (2026) Analyze 17,893 unaggregated product-level listed retail prices for external intimate-cleansing wipes 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… See the full description on the dataset page: https://huggingface.co/datasets/costinflation/feminine-wipe-prices-raw-dataset-2026.
17,893 raw U.S. feminine wipe price observations across 12 ZIP markets and 29 days.
Feminine Wipe Prices Raw Dataset (2026)
Analyze 17,893 unaggregated product-level listed retail prices for external intimate-cleansing wipes 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 5.2× the P10. Across compatible rows, the 40 wipes-equivalent price ranged from a P10 of $4.35 to a P90 of $22.72, a 5.2× 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 40 wipes. Compatible wipe and count units normalize to 40 wipes; unresolved counts remain blank. The dataset makes no health, hygiene, or efficacy claim. Original package fields remain available for audit.
Columns
Project ideas
- Compare 40-wipe package-equivalent prices
- Track matched product titles through time
- Compare selected ZIP markets
- Run reproducible EDA and missing-value analysis
Quick start with Pandas
import pandas as pd
df = pd.read_csv(
"costinflation-feminine-wipe-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 0 byte-identical input repeats.
- Scope review excluded 3 definite out-of-scope or unsafe titles representing 123 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). Feminine Wipe Prices Raw Dataset (2026). CC0 1.0 Universal.
Resources
Released by CostInflation Team.