--- 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 18,541 raw U.S. medical thermometer price observations across 12 ZIP markets and 29 days. # Thermometer Prices Raw Dataset (2026) ![Unbranded medical body thermometers against an abstract price-data backdrop.](dataset-cover-image.png) 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 ```python 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](https://costinflation.com/indices/thermometer-price-history) - The matching Hugging Face or Kaggle release will be linked after both destinations are verified. Released by **CostInflation Team**.