Instagram Topic Scraper
Pricing
Pay per event
Instagram Topic Scraper
Scrape and download Instagram posts based on the topic. Just add one or more Instagram topic URLs. Export scraped datasets, run the scraper via API, schedule and monitor runs or integrate with other tools.
0.0 (0)
Pricing
Pay per event
0
Monthly users
12
Runs succeeded
>99%
Last modified
5 days ago
You can access the Instagram Topic Scraper programmatically from your own 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 "directUrls": ["https://www.instagram.com/explore/topics/10155868806390727/sports/"],
10 "resultsLimit": 100,
11 "depthOfSubtopics": 1,
12}
13
14# Run the Actor and wait for it to finish
15run = client.actor("apify/instagram-topic-scraper").call(run_input=run_input)
16
17# Fetch and print Actor results from the run's dataset (if there are any)
18print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
19for item in client.dataset(run["defaultDatasetId"]).iterate_items():
20 print(item)
21
22# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start👁 Instagram Scraper API in Python
The Apify API client for Python is the official library that allows you to use Instagram Topic Scraper API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
pip install apify-clientOther API clients include:
Pricing
Pricing model
Pay per eventThis Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Actor start
$0.005
Flat charge for starting an Actor run.
Post
$0.002
Each post that is saved into the dataset.
