costinflation/pickle-prices-raw-dataset-2026
66,990 raw U.S. pickle price observations across 12 ZIP markets and 29 days. Pickle Prices Raw Dataset (2026) Analyze 66,990 unaggregated product-level listed retail prices for packaged pickles, including cucumber and other pickled produce 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:… See the full description on the dataset page: https://huggingface.co/datasets/costinflation/pickle-prices-raw-dataset-2026.
66,990 raw U.S. pickle price observations across 12 ZIP markets and 29 days.
Pickle Prices Raw Dataset (2026)
Analyze 66,990 unaggregated product-level listed retail prices for packaged pickles, including cucumber and other pickled produce 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
72.1% of rows expose a quantity-cleaning challenge. Only 18,672 of 66,990 rows (27.9%) have a compatible resolved listed package volume for the 16 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 16 fluid ounces. Compatible liquid-volume units normalize to a 16-fluid-ounce package basis. The scope includes cucumber and other pickled produce; this uses listed package volume, not drained product weight. Original package fields remain available for audit.
Columns
Project ideas
- Study package-volume price dispersion for pickles
- Run reproducible EDA and missing-value analysis
Quick start with Pandas
import pandas as pd
df = pd.read_csv(
"costinflation-pickle-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 95 byte-identical input repeats.
- Scope review excluded 3 definite out-of-scope or unsafe titles representing 20 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). Pickle Prices Raw Dataset (2026). CC0 1.0 Universal.
Resources
Released by CostInflation Team.