Facebook Marketplace Scraper
3 days trial then $39.00/month - No credit card required now
Facebook Marketplace Scraper
3 days trial then $39.00/month - No credit card required now
Scrape facebook marketplace with highly targeted/customized search queries and filters. Export and download data to CSV/JSON etc.
You can access the Facebook Marketplace 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 "listingUrls": [],
10 "searchUrls": [],
11 "proxy": {
12 "useApifyProxy": True,
13 "apifyProxyGroups": ["RESIDENTIAL"],
14 "apifyProxyCountry": "US",
15 },
16 "maxPage": 1,
17 "search": "",
18 "category": "vehicles",
19 "lat": "40.68",
20 "lng": "-73.87",
21 "radius": "10",
22 "maxConcurrency": 5,
23 "maxRequestsPerMinute": 200,
24}
25
26# Run the Actor and wait for it to finish
27run = client.actor("inquisitive_sarangi/facebook-marketplace-scraper").call(run_input=run_input)
28
29# Fetch and print Actor results from the run's dataset (if there are any)
30print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
31for item in client.dataset(run["defaultDatasetId"]).iterate_items():
32 print(item)
33
34# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-startFacebook Marketplace Scraper API in Python
The Apify API client for Python is the official library that allows you to use Facebook Marketplace Scraper API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
pip install apify-clientOther API clients include:
API MasterActor Metrics
25 monthly users
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8 bookmarks
92% runs succeeded
5.6 hours response time
Created in Sep 2023
Modified 14 days ago
