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Amazon Product Scraper

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Amazon Product Scraper

Amazon Product Scraper

junglee/amazon-crawler
Try for free

14 days trial then $40.00/month - No credit card required now

Use this Amazon scraper to collect data based on URL and country from the Amazon website. Extract product information without using the Amazon API, including reviews, prices, descriptions, and Amazon Standard Identification Numbers (ASINs). Download data in various structured formats.

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You can access the Amazon Product Scraper programmatically from your own Python applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {
9    "categoryOrProductUrls": [{ "url": "https://www.amazon.com/s?k=keyboard" }],
10    "maxItemsPerStartUrl": 100,
11    "proxyCountry": "AUTO_SELECT_PROXY_COUNTRY",
12    "maxOffers": 0,
13}
14
15# Run the Actor and wait for it to finish
16run = client.actor("junglee/amazon-crawler").call(run_input=run_input)
17
18# Fetch and print Actor results from the run's dataset (if there are any)
19print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
20for item in client.dataset(run["defaultDatasetId"]).iterate_items():
21    print(item)
22
23# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

📦 Amazon Product Scraper API in Python

The Apify API client for Python is the official library that allows you to use Amazon Product Scraper API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

pip install apify-client

Other API clients include:

Developer
Maintained by Apify
Actor metrics
  • 267 monthly users
  • 77 stars
  • 96.9% runs succeeded
  • 2.8 days response time
  • Created in Oct 2018
  • Modified 16 days ago
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