Rebecca Minkoff Scraper
No credit card required
Rebecca Minkoff Scraper
No credit card required
Scrape Rebecca Minkoff and extract data on clothing accessories from rebeccaminkoff.com. Our Rebecca Minkoff API lets you crawl product information and pricing. The saved data can be downloaded as HTML, JSON, CSV, Excel, and XML.
You can access the Rebecca Minkoff 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 "maxRequestsPerCrawl": 20,
10 "extendOutputFunction": """async ({ data, item, product, images, fns, name, request, variants, context, customData, input, Apify }) => {
11 return item;
12}""",
13 "extendScraperFunction": """async ({ fns, customData, Apify, label }) => {
14
15}""",
16 "customData": {},
17 "maxConcurrency": 20,
18 "maxRequestRetries": 3,
19}
20
21# Run the Actor and wait for it to finish
22run = client.actor("mshopik/rebecca-minkoff-scraper").call(run_input=run_input)
23
24# Fetch and print Actor results from the run's dataset (if there are any)
25print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
26for item in client.dataset(run["defaultDatasetId"]).iterate_items():
27 print(item)
28
29# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-startRebecca Minkoff Scraper API in Python
The Apify API client for Python is the official library that allows you to use Rebecca Minkoff Scraper API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
pip install apify-clientOther API clients include:
Mark CarterActor Metrics
1 monthly user
-
1 star
>99% runs succeeded
Created in Nov 2021
Modified 2 years ago
