README.md

---
license: cc0-1.0
language:
- en
task_categories:
- tabular-regression
tags:
- retail
- prices
- time-series
- raw-data
- food
pretty_name: Onion Prices Raw Dataset (2026)
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: costinflation-onion-retail-prices-raw-2026-07-13-to-2026-08-10.csv
dataset_info:
  features:
  - name: series_id
    dtype: string
  - name: series_title
    dtype: string
  - name: canonical_url
    dtype: string
  - name: geography_type
    dtype: string
  - name: geography_id
    dtype: string
  - name: geography_label
    dtype: string
  - name: observed_date
    dtype: string
  - name: product_name
    dtype: string
  - name: quantity_value
    dtype: float64
  - name: quantity_unit
    dtype: string
  - name: quantity_name
    dtype: string
  - name: price_amount
    dtype: float64
  - name: currency_code
    dtype: string
  - name: normalized_price_amount
    dtype: float64
  - name: normalized_quantity_value
    dtype: float64
  - name: normalized_quantity_unit
    dtype: string
  splits:
  - name: train
    num_examples: 17274
---

> 17,274 raw U.S. onion price observations across 12 ZIP markets and 29 days.

# Onion Prices Raw Dataset (2026)

![CostInflation cover for fresh common onions: a centered ON monogram over a source-neutral retail-price data backdrop.](dataset-cover-image.png)

Analyze **17,274 unaggregated product-level listed retail prices** for fresh common onions 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

| Field | Coverage |
|---|---:|
| Rows | 17,274 |
| Columns | 16 |
| Observation window | July 13–August 10, 2026 |
| Dates / ZIP markets | 29 / 12 |
| Market-days | 348 of 348 |
| Distinct product titles | 92 |
| Comparable-price rows | 12,130 (70.2%) |
| Rows with unresolved comparison | 5,144 (29.8%) |
| Comparison target | 1 pound |
| Format / currency | CSV / USD |
| Expected updates | None; fixed research snapshot |

## A result worth investigating

**29.8% of rows expose a quantity-cleaning challenge.** Only 12,130 of 17,274 rows (70.2%) have a compatible resolved fresh-onion mass for the 1 pound 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 **1 pound**. Compatible mass units normalize to one pound. The public scope is fresh common onions; preserved onions, planting products, green onions, and shallots are excluded during exact-title review. Original package fields remain available for audit.

## Columns

| Column | Type | Meaning |
|---|---|---|
| `series_id` | string | Stable source-neutral dataset series key |
| `series_title` | string | Human-readable series title |
| `canonical_url` | string | Related CostInflation category page |
| `geography_type` | string | Geography level; always postal_code |
| `geography_id` | string | Five-digit U.S. ZIP code; load as text |
| `geography_label` | string | Human-readable ZIP-market label |
| `observed_date` | date | Observation date in YYYY-MM-DD |
| `product_name` | string | Full observed title; not a stable product ID |
| `quantity_value` | number | Resolved package quantity; 0 if unresolved |
| `quantity_unit` | string | Resolved package unit or unknown |
| `quantity_name` | string | Human-readable package quantity |
| `price_amount` | number | Listed package or unit price |
| `currency_code` | string | Currency; always USD |
| `normalized_price_amount` | nullable number | Listed price scaled to the dataset comparison target |
| `normalized_quantity_value` | number | Numeric comparison target |
| `normalized_quantity_unit` | string | Unit for the comparison target |

## Project ideas

- Compare one-pound-equivalent fresh-onion prices
- Track matched product titles through time
- Run reproducible EDA and missing-value analysis

## Quick start with Pandas

```python
import pandas as pd

df = pd.read_csv(
    "costinflation-onion-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 25 definite out-of-scope or unsafe titles representing 5,179 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). *Onion Prices Raw Dataset (2026).* CC0 1.0 Universal.

## Resources

- [Onions price history](https://costinflation.com/indices/onion-price-history)
- [Kaggle dataset](https://www.kaggle.com/datasets/costinflation/onion-prices-raw-dataset-2026)
- [Hugging Face dataset](https://huggingface.co/datasets/costinflation/onion-prices-raw-dataset-2026)

Released by **CostInflation Team**.