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Facebook Marketplace Scraper

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Facebook Marketplace Scraper

Facebook Marketplace Scraper

datavoyantlab/facebook-marketplace-scraper
Try for free

3 days trial then $29.00/month - No credit card required now

Scrape Facebook Marketplace listings easily. Extract prices, photos, seller info & more. Export to JSON, CSV, Excel or use API for real-time data. Perfect for market research & e-commerce analysis.

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    "url": "https://www.facebook.com/marketplace/category/vehicles",
10    "proxy": {
11        "useApifyProxy": True,
12        "apifyProxyGroups": ["RESIDENTIAL"],
13        "apifyProxyCountry": "US",
14    },
15}
16
17# Run the Actor and wait for it to finish
18run = client.actor("datavoyantlab/facebook-marketplace-scraper").call(run_input=run_input)
19
20# Fetch and print Actor results from the run's dataset (if there are any)
21print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
22for item in client.dataset(run["defaultDatasetId"]).iterate_items():
23    print(item)
24
25# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Facebook Marketplace Scraper (Fast) 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-client

Other API clients include:

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Maintained by Community

Actor Metrics

  • 13 monthly users

  • 4 stars

  • >99% runs succeeded

  • 0.65 hours response time

  • Created in Sep 2024

  • Modified 3 days ago