costinflation/457k-prices-build-a-burger

457,352 prices: 10 categories, 12 U.S. ZIPs, 29 days

Burger Ingredient Prices Raw Dataset (2026)

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How do listed and package-standardized prices for common burger components differ across U.S. ZIP markets and days?

This fixed research snapshot contains 457,352 unaggregated, quality-filtered price observations across 10 burger-component 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 a source-neutral package-equivalent price field when quantity is resolved.

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 burger-component category. It is not a sale, order, demand, inventory, market-share, recipe, or nutrition record. Multiple distinct offers can share a title, ZIP, and date; exact duplicate public rows have been removed.

Comparable-price method

price_amount preserves the listed price. Where a compatible package quantity is resolved, normalized_price_amount scales that price to the category-specific comparison target recorded in normalized_quantity_value and normalized_quantity_unit.

Compare normalized values within the same series_id. Rows whose quantity is unresolved or incompatible retain the listed price and leave normalized_price_amount blank. The comparison targets are package-equivalent research units, not a recipe or serving recommendation.

Category reference

Columns

Project ideas

• —Compare within-category package-equivalent price distributions across the 12 ZIP markets.
• —Measure how daily medians and dispersion change across the 29-day window.
• —Build clearly labeled shopping-basket scenarios from the category-specific comparison targets.
• —Model which product-title and package formats are most likely to have unresolved comparable prices.

Quick start with Pandas

import pandas as pd

df = pd.read_csv(
    "costinflation-build-a-burger-prices-2026-07-21-to-2026-08-18.csv",
    dtype={"geography_id": "string", "series_id": "category"},
)
burger_core = df.query(
    "series_id in ['hamburger_bun', 'beef_patties', 'american_cheese']"
).dropna(subset=["normalized_price_amount"])
print(burger_core.groupby(["series_id", "geography_label"])["normalized_price_amount"].median())

Quality and interpretation

• —All 29 expected dates, all 12 ZIP markets, and all 3,480 category × ZIP × date cells are present.
• —Exact duplicate public rows: 0.
• —Formula-leading product titles: 0.
• —Every nonblank comparable price has a positive resolved quantity compatible with its category-specific 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, consumption, product performance, and coverage outside the selected ZIP panel 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). Burger Ingredient Prices Raw Dataset (2026). CC0 1.0 Universal.

Released by CostInflation Team.

Tags: Retail and Shopping; Economics; United States; Time Series Analysis; Data Visualization

License: CC0 1.0 Universal

Update frequency: Never