This Python library allows you to interface with the API at Keepa to query for Amazon product information and history. It also contains a plotting module to allow for plotting of a product.

See API pricing at Keepa API.

Documentation can be found on readthedocs at keepa Documentation.

This library is compatible with Python >= 3.8 and requires:

• numpy
• aiohttp
• matplotlib
• tqdm

Product history can be plotted from the raw data when matplotlib is installed.

Interfacing with the keepa requires an access key and a monthly subscription from Keepa API.

Module can be installed from PyPi with:

pip install keepa

Source code can also be downloaded from GitHub and installed using:

cd keepa
pip install .

import keepa
accesskey = 'XXXXXXXXXXXXXXXX' # enter real access key here
api = keepa.Keepa(accesskey)

# Single ASIN query
products = api.query('B0088PUEPK') # returns list of product data

# Plot result (requires matplotlib)
keepa.plot_product(products[0])

Here's an example of obtaining a product and plotting its price and offer history using the keepa.AsyncKeepa class:

>>> import asyncio
>>> import keepa
>>> product_parms = {'author': 'jim butcher'}
>>> async def main():
...     key = '<REAL_KEEPA_KEY>'
...     api = await keepa.AsyncKeepa().create(key)
...     return await api.product_finder(product_parms)
>>> asins = asyncio.run(main())
>>> asins
['B000HRMAR2',
 '0578799790',
 'B07PW1SVHM',
...
 'B003MXM744',
 '0133235750',
 'B01MXXLJPZ']

Query for product with ASIN 'B0088PUEPK' using the asynchronous keepa interface.

>>> import asyncio
>>> import keepa
>>> async def main():
...     key = '<REAL_KEEPA_KEY>'
...     api = await keepa.AsyncKeepa().create(key)
...     return await api.query('B0088PUEPK')
>>> response = asyncio.run(main())
>>> response[0]['title']
'Western Digital 1TB WD Blue PC Internal Hard Drive HDD - 7200 RPM,
SATA 6 Gb/s, 64 MB Cache, 3.5" - WD10EZEX'

Import interface and establish connection to server

import keepa
accesskey = 'XXXXXXXXXXXXXXXX' # enter real access key here
api = keepa.Keepa(accesskey)

Single ASIN query

products = api.query('059035342X')

# See help(api.query) for available options when querying the API

You can use keepa witch async / await too

import keepa
accesskey = 'XXXXXXXXXXXXXXXX' # enter real access key here
api = await keepa.AsyncKeepa.create(accesskey)

Single ASIN query (async)

products = await api.query('059035342X')

Multiple ASIN query from List

asins = ['0022841350', '0022841369', '0022841369', '0022841369']
products = api.query(asins)

Multiple ASIN query from numpy array

asins = np.asarray(['0022841350', '0022841369', '0022841369', '0022841369'])
products = api.query(asins)

Products is a list of product data with one entry per successful result from the Keepa server. Each entry is a dictionary containing the same product data available from Amazon.

# Available keys
print(products[0].keys())

# Print ASIN and title
print('ASIN is ' + products[0]['asin'])
print('Title is ' + products[0]['title'])

The raw data is contained within each product result. Raw data is stored as a dictionary with each key paired with its associated time history.

# Access new price history and associated time data
newprice = products[0]['data']['NEW']
newpricetime = products[0]['data']['NEW_time']

# Can be plotted with matplotlib using:
import matplotlib.pyplot as plt
plt.step(newpricetime, newprice, where='pre')

# Keys can be listed by
print(products[0]['data'].keys())

The product history can also be plotted from the module if matplotlib is installed

keepa.plot_product(products[0])

You can obtain the offers history for an ASIN (or multiple ASINs) using the offers parameter. See the documentation at Request Products for further details.

products = api.query(asins, offers=20)
product = products[0]
offers = product['offers']

# each offer contains the price history of each offer
offer = offers[0]
csv = offer['offerCSV']

# convert these values to numpy arrays
times, prices = keepa.convert_offer_history(csv)

# for a list of active offers, see
indices = product['liveOffersOrder']

# with this you can loop through active offers:
indices = product['liveOffersOrder']
offer_times = []
offer_prices = []
for index in indices:
    csv = offers[index]['offerCSV']
    times, prices = keepa.convert_offer_history(csv)
    offer_times.append(times)
    offer_prices.append(prices)

# you can aggregate these using np.hstack or plot at the history individually
import matplotlib.pyplot as plt
for i in range(len(offer_prices)):
    plt.step(offer_times[i], offer_prices[i])
plt.show()

If you plan to do a lot of simulatneous query, you might want to speedup query using wait=False arguments.

products = await api.query('059035342X', wait=False)

To load used buy box statistics, you have to enable offers. This example loads in product offers and converts the buy box data into a pandas.DataFrame.

>>> import keepa
>>> key = '<REAL_KEEPA_KEY>'
>>> api = keepa.Keepa(key)
>>> response = api.query('B0088PUEPK', offers=20)
>>> product = response[0]
>>> buybox_info = product['buyBoxUsedHistory']
>>> df = keepa.process_used_buybox(buybox_info)
               datetime         user_id         condition  isFBA
0   2022-11-02 16:46:00  A1QUAC68EAM09F   Used - Like New   True
1   2022-11-13 10:36:00  A18WXU4I7YR6UA  Used - Very Good  False
2   2022-11-15 23:50:00   AYUGEV9WZ4X5O   Used - Like New  False
3   2022-11-17 06:16:00  A18WXU4I7YR6UA  Used - Very Good  False
4   2022-11-17 10:56:00   AYUGEV9WZ4X5O   Used - Like New  False
..                  ...             ...               ...    ...
115 2023-10-23 10:00:00   AYUGEV9WZ4X5O   Used - Like New  False
116 2023-10-25 21:14:00  A1U9HDFCZO1A84   Used - Like New  False
117 2023-10-26 04:08:00   AYUGEV9WZ4X5O   Used - Like New  False
118 2023-10-27 08:14:00  A1U9HDFCZO1A84   Used - Like New  False
119 2023-10-27 12:34:00   AYUGEV9WZ4X5O   Used - Like New  False

Contribute to this repository by forking this repository and installing in development mode with:

git clone https://github.com/<USERNAME>/keepa
pip install -e .[test]

You can then add your feature or commit your bug fix and then run your unit testing with:

pytest

Unit testing will automatically enforce minimum code coverage standards.

Next, to ensure your code meets minimum code styling standards, run:

pre-commit run --all-files

Finally, create a pull request from your fork and I'll be sure to review it.

This Python module, written by Alex Kaszynski and several contribitors, is based on Java code written by Marius Johann, CEO Keepa. Java source is can be found at keepacom/api_backend.

Apache License, please see license file. Work is credited to both Alex Kaszynski and Marius Johann.