costinflation/119k-prices-petflation-2026

119,316 prices: 11 categories, 12 U.S. ZIPs, 29 days

119K Prices: Petflation 2026

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How do pet food, treats, litter, grooming, cleanup supplies, and training-pad prices compare across U.S. ZIP markets when package quantities are standardized within each category?

This fixed research snapshot contains 119,316 unaggregated, quality-filtered retail price observations across 11 pet-care categories, 12 U.S. ZIP markets, and 29 consecutive dates from July 21 through August 18, 2026. The analysis-ready CSV preserves product titles, dates, geography, listed package quantities and prices, plus source-neutral category-specific comparable-price fields.

What “quality-filtered” means here: the rows are unaggregated product-level observations after category, safety, and exact-duplicate filtering. The file includes derived comparison fields and is a prepared research snapshot rather than an aggregate index.

Dataset at a glance

What one row represents

One row is one quality-filtered product-title, ZIP-market, date, package-quantity, and listed-price observation in one pet-care category. It is not a sale, order, demand, inventory, market-share, or consumer record. Multiple distinct offers can share a title, ZIP, and date; exact duplicate public rows have been removed.

Comparable-price method

priceamount preserves the listed price. Where a compatible package quantity is resolved, normalizedpriceamount scales that price to the category-specific target shown in normalizedquantityvalue and normalizedquantity_unit.

Comparison targets differ by category. Compare normalized values within the same seriesid; do not compare normalized prices directly across categories with different targets or quantity dimensions. Rows whose quantity is unresolved or incompatible retain their listed price and leave normalizedprice_amount blank.

Category reference

Columns

Project ideas

• —Compare dry and wet dog-food or cat-food price distributions without mixing category targets.
• —Measure geographic variation within litter, treats, grooming, cleanup, or training supplies.
• —Track daily within-category medians through the 29-day observation window.
• —Analyze which pet-care categories have the widest comparable-price dispersion.
• —Model which package formats are most likely to yield a compatible comparison quantity.

Quick start with Pandas

import pandas as pd

df = pd.readcsv( "costinflation-petflation-prices-2026-07-21-to-2026-08-18.csv", dtype={"geographyid": "string", "seriesid": "category"}, ) drydogfood = df.query("seriesid == 'drydogfood'").dropna( subset=["normalizedpriceamount"] ) print(drydogfood.groupby("geographylabel")["normalizedprice_amount"].median())

Quality and interpretation

• —All 29 expected dates and all 12 ZIP markets are present.
• —3,827 of 3,828 expected category × ZIP × date cells are present. The one empty cell is dog training pads in ZIP 39206 on July 31, 2026.
• —Exact duplicate public rows: 0.
• —Formula-leading product titles: 0.
• —Every nonblank comparable price has a positive resolved quantity compatible with its category target.
• —Product titles are descriptive text, not stable public product identifiers.
• —ZIP labels describe selected markets rather than citywide, metropolitan, state, or national estimates.
• —Shipping, tax, redeemed promotions, purchases, product performance, and veterinary outcomes are outside this dataset.

Provenance

CostInflation prepared and released this fixed, source-neutral snapshot. Category assignment, geographic labels, exact-duplicate filtering, safety filtering, and package-equivalent calculations were applied consistently before publication. The public file contains only the 16 documented research columns.

License

Released under CC0 1.0 Universal for unrestricted reuse. Attribution is not required, but citation helps others find the release.

CostInflation Team. (2026). 119K Prices: Petflation 2026. CC0 1.0 Universal.

Released by CostInflation Team.