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๐ดUber Eats Scraper [RENTAL]
3 days trial then $35.00/month - No credit card required now
๐ดUber Eats Scraper [RENTAL]
3 days trial then $35.00/month - No credit card required now
Rental - Fast and cost-effective Uber Eats Scraper requiring just a search query and location. Instantly retrieve comprehensive restaurant data, including names, ratings, locations, and menus. Seamlessly integrates with tools like Google Sheets, and more, offering a highly efficient data solution.
You can access the ๐ดUber Eats Scraper [RENTAL] 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, youโll 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 "query": "Pizza",
10 "address": "1600 Pennsylvania Avenue, Washington DC",
11 "locale": "en-US",
12 "maxRows": 0,
13 "proxy": {
14 "useApifyProxy": True,
15 "apifyProxyGroups": ["RESIDENTIAL"],
16 },
17}
18
19# Run the Actor and wait for it to finish
20run = client.actor("borderline/ubereats-scraper").call(run_input=run_input)
21
22# Fetch and print Actor results from the run's dataset (if there are any)
23print("๐พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
24for item in client.dataset(run["defaultDatasetId"]).iterate_items():
25 print(item)
26
27# ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/python/docs/quick-start๐ดUber Eats Scraper [RENTAL] API in Python
The Apify API client for Python is the official library that allows you to use ๐ดUber Eats Scraper [RENTAL] API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
pip install apify-clientOther API clients include:
borderlineActor Metrics
12 monthly users
-
6 bookmarks
>99% runs succeeded
6.4 hours response time
Created in Sep 2024
Modified 3 days ago
