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To track Amazon prices with Python, collect timestamped price histories for a fixed set of products, save the raw observations, and compare the same basket over time. The result is a useful Amazon product-basket index—not a measure of economy-wide inflation and not a replacement for the official Consumer Price Index (CPI).
How can I track Amazon prices with Python?
A practical workflow has four parts: choose a consistent product basket, obtain historical observations, store the raw data, and calculate a clearly labeled index. Keepa offers an API and a community-developed Python client for historical Amazon product data. Its documentation describes the service as delivering price histories for products and offers from international Amazon marketplaces to applications. Keepa API documentation
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For a quick check on one item, Amazon also offers a consumer-facing price-history feature. Its page describes 30-, 90-, and 365-day views and uses questions shoppers might ask, including “What’s the price history?” and “Is this the lowest price recently?” Availability and rollout can change; Amazon lists the feature for customers in the US, UK, Canada, and India, while its wording about the 365-day rollout specifically names the US, UK, and India. Amazon price history
Choose the data route that fits the job
| Approach | Best for | Access and scope |
|---|---|---|
| Keepa API with Python | Building a repeatable dataset, examining historical observations, and calculating a custom basket index | Requires an API key and token-based access; request costs depend on the query. The Python client is community-developed, not an official Keepa client. Keepa API documentation and keepa Python client documentation |
| Amazon price history | Checking the history of an individual item without building a dataset | Consumer-facing feature with the windows and regional availability Amazon describes on its page; this is not a custom basket export workflow. Amazon price history |
| Amazon Product Pricing API | Seller pricing and offer-management solutions | Amazon marks the API “Sellers only”; it is not the default consumer route for this tutorial. The documentation lists version v2022-05-01. Amazon Product Pricing API use-case guide |
Define a basket that stays comparable
Before collecting data, write down exactly what counts as each product and each price. A product changing package size, condition, seller, or listing can create an apparent price movement that is not a like-for-like comparison.
- Product identity: Record the ASIN and a human-readable name. Decide how you will handle a discontinued listing or a materially changed product; silently substituting a replacement breaks continuity.
- Marketplace and currency: Record the Amazon marketplace or locale, such as the US or UK store, and the currency returned. Do not combine currencies without a separately documented conversion method.
- Price basis: Define whether your measure is the displayed product price or another price field. Note whether shipping, tax, coupons, and condition are included. If the source does not provide a component consistently, do not imply that your measure includes it.
- Observation cadence: Choose a collection interval that your API token budget can sustain, then use it consistently. A daily series and an occasional series do not sample price changes in the same way.
- Basket weights: Decide whether every product counts equally or whether you have defensible weights. An equal-weight calculation is simple, but it does not reflect household spending patterns unless those weights happen to match.
Set up the Keepa Python client
The community-developed keepa package documents installation with pip install keepa, product queries, and timestamped history arrays. Keepa’s API requires an API key and accounts for requests with a token bucket; its documentation reports token availability in response fields such as tokensLeft. The basic product request is currently documented as costing one token per requested product, while optional offer retrieval costs more. Check the current API documentation and your account’s token balance before deciding how frequently to query. Keepa API documentation keepa Python client documentation
- Install the client: In your project environment, run
pip install keepa. - Keep the API key out of source control: Load it from an environment variable or another secret store rather than putting it directly in a notebook or committed script.
- Query a product: The client documentation demonstrates a product query with
history=True; it also showsstats=90as an optional example, not a requirement. Use the query options suited to your analysis and inspect the returned object before building assumptions into your script. keepa Python client documentation - Inspect the history arrays: Confirm which arrays contain the price series you intend to analyze and how their timestamps are represented. Convert the returned timestamps carefully before grouping observations by day, week, or month.
- Budget requests: Check request costs and token-bucket response fields, especially if requesting offer data or expanding from a few products to a larger basket. Do not schedule queries more often than the available tokens allow.
Save observations before calculating changes
Persist raw observations in a CSV file or database before summarizing them. Keeping the original values lets you revise cleaning rules or calculations without having to reconstruct the series. A useful record includes:
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- ASIN and product name;
- marketplace or locale and currency;
- observation timestamp, with a consistent time zone;
- the price field and its value;
- the product condition or offer context if available;
- whether shipping, tax, or coupon effects are represented; and
- the source and any missing-data or collection notes.
Use pandas to organize observations, NumPy for numerical calculations, and Matplotlib to plot the result; these are common analysis tools for the workflows described by the client documentation. Preserve null or missing observations as missing rather than filling them with zero. A zero price would mean something very different from a price that was not collected.
Calculate a custom basket index
For a fixed basket with prices available in both the base period and a later period, an unweighted index can be calculated as:
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custom basket index = (current basket total / base-period basket total) × 100
If the base-period total is 100 in the basket’s currency, a later total of 108 produces an index of 108: the defined basket costs 8% more than it did in that base period. That statement applies only to the products, price definitions, locale, and observations in your dataset. Call it your Amazon basket index or custom product-basket index, not CPI or economy-wide inflation.
For a valid total, make the same products and price basis contribute in both periods. If one product is missing, report how you handled it; excluding it can change the basket’s composition. Do not silently replace a missing value with the most recent price, zero, or a new product. If you use weights, disclose them and apply the same weights in both periods.
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Interpret the result without overstating it
A marketplace product basket can show how the selected Amazon listings’ prices moved under your chosen rules. It does not represent every product people buy, all retailers, household spending shares, or the full set of price changes measured by an official CPI. Changes in shipping, tax treatment, coupons, seller, condition, package size, and product availability can also affect what your series appears to show.
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Collection frequency matters when comparing price datasets. An IMF CPI Manual search-result excerpt cautions that differences between the frequency of web-scraped prices and traditional price collection should be accounted for, and that ignoring frequency differences can underestimate inflation. Treat that as a narrow methodological warning rather than a complete CPI method. IMF Consumer Price Index Manual
If you compare your index with published CPI, align the time periods and state plainly that the two measures cover different baskets and collection methods. A difference between them does not by itself show that either measure is wrong.
When to use Amazon’s built-in history instead
If you only want to answer “Has this item been on sale in the past 30 days?” or “What’s the price history?”, Amazon’s own interface may be more convenient than assembling a Python dataset. Amazon’s page describes 30-, 90-, and 365-day views, with the 365-day rollout wording naming the US, UK, and India. Because feature access and historical windows can change, check the current listing and help information for your marketplace. Amazon price history
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Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
