Datasets:

costinflation
/

thermometer-prices-raw-dataset-2026

license: cc0-1.0
language:
  - en
task_categories:
  - tabular-regression
tags:
  - retail
  - prices
  - time-series
  - raw-data
  - medical-devices
pretty_name: Thermometer Prices Raw Dataset (2026)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: >-
          costinflation-thermometer-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: 18541

18,541 raw U.S. medical thermometer price observations across 12 ZIP markets and 29 days.

Thermometer Prices Raw Dataset (2026)

Analyze 18,541 unaggregated product-level listed retail prices for medical body thermometers 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	18,541
Columns	16
Observation window	July 13–August 10, 2026
Dates / ZIP markets	29 / 12
Market-days	348 of 348
Distinct product titles	213
Comparable-price rows	18,541 (100.0%)
Rows with unresolved comparison	0 (0.0%)
Comparison target	1 thermometer
Format / currency	CSV / USD
Expected updates	None; fixed research snapshot

A result worth investigating

Listed-price P90 was 6.0× the P10. Across one-unit observations, listed prices ranged from a P10 of $6.99 to a P90 of $41.90, a 6.0× spread. This is a title-level retail distribution, not a claim of product, clinical, installation, or performance equivalence.

What one row represents

One row is one quality-filtered product-title, ZIP-market, date, package-quantity, and listed-price observation. These are listed retail prices—not sales, orders, revenue, demand, inventory, market share, or consumer behavior. Multiple distinct offers can share a title, ZIP, and date; exact duplicate public rows have been removed.

Comparable-price method

The fixed comparison target is 1 thermometer. This is one-unit listed-price comparability, not clinical-performance or diagnostic equivalence. No health claim is made. The original package price and quantity remain in every row, so users can audit or replace the provided comparison.

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

Explore listed-price dispersion across device titles
Track matched thermometer titles through time
Study cross-market listed-price parity
Build a medical-device retail dashboard
Run EDA in Python, R, SQL, Excel, Tableau, or Power BI
Benchmark missing-value handling without discarding listed-price observations

Quick start with Pandas

import pandas as pd

df = pd.read_csv(
    "costinflation-thermometer-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 2 byte-identical input repeats.
Scope review excluded 14 definite out-of-scope or unsafe titles representing 206 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). Thermometer Prices Raw Dataset (2026). CC0 1.0 Universal.

Resources

Thermometer price history
The matching Hugging Face or Kaggle release will be linked after both destinations are verified.

Released by CostInflation Team.