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Facebook Likes Scraper

Try for free

Pay $5.00 for 1,000 Likers

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Facebook Likes Scraper

Facebook Likes Scraper

apify/facebook-likes-scraper
Try for free

Pay $5.00 for 1,000 Likers

Extract Facebook likes data from one or multiple Facebook posts. Get post URL, reaction type (like, love, care, sad, angry, laugh), and basic liker info such as Facebook name and profile URL. Download the data in JSON, CSV Excel and use it in apps, spreadsheets, and reports.

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You can access the Facebook Likes 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    "startUrls": [{ "url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl" }],
10    "resultsLimit": 10,
11}
12
13# Run the Actor and wait for it to finish
14run = client.actor("apify/facebook-likes-scraper").call(run_input=run_input)
15
16# Fetch and print Actor results from the run's dataset (if there are any)
17print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
18for item in client.dataset(run["defaultDatasetId"]).iterate_items():
19    print(item)
20
21# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

❤︎ Facebook Likes Scraper API in Python

The Apify API client for Python is the official library that allows you to use Facebook Likes 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
  • 69 monthly users
  • 9 stars
  • 99.8% runs succeeded
  • 6.8 days response time
  • Created in Apr 2023
  • Modified about 12 hours ago