Multi-Platform Sneaker Data Scraping: Practical API Application and Standardization for billys_tokyo and atoms

In previous articles, we have introduced the basics of the Sneaker API, advanced data parsing and interface optimization, as well as the development of real-time data monitoring and price comparison tools. In this article, we will focus on how to perform API calls and scrape data from specific platforms (such as billys_tokyo and atoms) and provide practical case studies and code examples to help you get started quickly in real-world development.

1. Overview of Data Platforms and Features

billys_tokyo Platform

billys_tokyo is a sales platform specializing in trendy sneakers, with a beautifully designed website and rich product information. The data on the platform updates quickly, including detailed product information (such as brand, model, price, stock status, etc.), although some fields may have platform-specific labels or formats.

atoms Platform

atoms, another well-known sneaker data platform, offers a diverse product display and a relatively standardized data structure. While the data on atoms may differ slightly from billys_tokyo, it also covers the basic product information and is suitable for integration with other platform data to implement price comparison functionality.

The data formats from these platforms may vary slightly, so we need to customize the API calls and parsing according to the characteristics of each platform's API and map the data into a unified data model.

2. billys_tokyo Data Scraping in Practice

Here is an example of how to call the billys_tokyo API and parse the product data using Python. This code helps you understand how to set up basic error handling and field parsing strategies to ensure stable data scraping:

import requests
import json
def fetch_billys_tokyo_data(url):
headers = {
'X-Luckdata-Api-Key': 'your_key'
}
try:
response = requests.get(url, headers=headers, timeout=10)
if response.status_code == 200:
data = response.json()
# Debugging output: Observe the data structure
print(json.dumps(data, indent=2, ensure_ascii=False))
# Extract key fields based on actual returned data structure
product = {
'brand': data.get('brand', 'Unknown brand'),
'model': data.get('model', 'Unknown model'),
'price': data.get('price', 'Unknown price'),
'stock': data.get('stock', 'No stock information')
}
return product
else:
print(f"Error: Received status code {response.status_code}")
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
return None
# Example call
billys_url = 'https://luckdata.io/api/sneaker-API/get_7go9?url=https://www.billys-tokyo.net/shop/g/g6383800022045/'
product_billys = fetch_billys_tokyo_data(billys_url)
print("billys_tokyo data:", product_billys)

In this code, we first set up basic error handling to prevent the program from crashing due to network request issues. For the returned data, we check if the fields exist and provide default values for each field, ensuring that the program continues to run smoothly even if some data is missing.

3. atoms Data Scraping in Practice

The data structure on atoms is relatively standardized, but we still need to pay attention to differences in field names. Here is an example of how to parse the data based on the atoms structure, with added fault tolerance design:

import requests
import json
def fetch_atoms_data(url):
headers = {
'X-Luckdata-Api-Key': 'your_key'
}
try:
response = requests.get(url, headers=headers, timeout=10)
if response.status_code == 200:
data = response.json()
# Display JSON structure for reference
print(json.dumps(data, indent=2, ensure_ascii=False))
product = {
'brand': data.get('productInfo', {}).get('brand', 'Unknown brand'),
'model': data.get('productInfo', {}).get('model', 'Unknown model'),
'price': data.get('pricing', {}).get('retailPrice') or data.get('pricing', {}).get('discountedPrice', 'Unknown price'),
'stock': data.get('inventory', {}).get('available', 'No stock information')
}
return product
else:
print(f"Error: Received status code {response.status_code}")
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
return None
# Example call
atoms_url = 'https://luckdata.io/api/sneaker-API/get_atoms_sample?url=https://www.atoms-example.com/product/12345'
product_atoms = fetch_atoms_data(atoms_url)
print("atoms data:", product_atoms)

This code demonstrates how to parse data based on the returned structure from atoms. We further provide a fallback mechanism for the price field to handle variations in the data structure.

4. Multi-Platform Asynchronous Scraping and Integration

When dealing with data from multiple platforms simultaneously, using asynchronous requests (asyncio + aiohttp) can significantly improve efficiency. The following code demonstrates a standardized integration process, introducing simple fault tolerance and integration strategies:

import asyncio
import aiohttp
def normalize_product_data(raw_data, source):
if source == 'billys_tokyo':
return {
'brand': raw_data.get('brand', 'Unknown brand'),
'model': raw_data.get('model', 'Unknown model'),
'price': raw_data.get('price', 'Unknown price'),
'stock': raw_data.get('stock', 'No stock information')
}
elif source == 'atoms':
return {
'brand': raw_data.get('productInfo', {}).get('brand', 'Unknown brand'),
'model': raw_data.get('productInfo', {}).get('model', 'Unknown model'),
'price': raw_data.get('pricing', {}).get('retailPrice') or raw_data.get('pricing', {}).get('discountedPrice', 'Unknown price'),
'stock': raw_data.get('inventory', {}).get('available', 'No stock information')
}
return {}
async def fetch_data(session, url, source):
try:
async with session.get(url, headers={'X-Luckdata-Api-Key': 'your_key'}) as response:
data = await response.json()
return normalize_product_data(data, source)
except Exception as e:
print(f"{source} fetch failed: {e}")
return {}
async def fetch_all_products(urls_sources):
async with aiohttp.ClientSession() as session:
tasks = [fetch_data(session, url, source) for url, source in urls_sources]
return await asyncio.gather(*tasks)
# Multi-platform sources configuration
urls_sources = [
('https://luckdata.io/api/sneaker-API/get_7go9?url=https://www.billys-tokyo.net/shop/g/g6383800022045/', 'billys_tokyo'),
('https://luckdata.io/api/sneaker-API/get_atoms_sample?url=https://www.atoms-example.com/product/12345', 'atoms')
]
products = asyncio.run(fetch_all_products(urls_sources))
print("Multi-platform standardized data:", products)

This code demonstrates how to use asynchronous requests to improve scraping efficiency. After standardizing the data, we return it and use exception handling to ensure that other requests continue to run even if some fail.

5. Practical Application Scenarios and Future Directions

After completing the basic scraping and standardization, you can further integrate the data into price comparison systems, user notification mechanisms, and data analysis modules. Common applications include:

• Multi-platform price comparison services: Help users select the best deals.

• Out-of-stock monitoring and restock notifications: Notify users when stock becomes available again.

• Historical price trend analysis: Predict potential price trends.

Through these applications, you can turn raw API data into valuable practical tools and truly implement data-driven sneaker application development.

Conclusion

Mastering API calls and data scraping techniques from different data platforms is the foundation for building efficient, stable, and scalable sneaker data applications. This article demonstrated how to apply API calls, standardize data structures, and implement asynchronous scraping through practical examples from billys_tokyo and atoms. In the future, you can further extend multi-platform integration and price prediction functionalities to build your own Sneaker Intelligence System.

Articles related to APIs :

• A Comprehensive Guide to Sneaker API: Your Ultimate Tool for Sneaker Data Access

• Free Sneaker API Application: A Detailed Guide and Usage Introduction

• Advanced Data Parsing and API Optimization: Building a More Efficient Sneaker API Application

• How to Enhance Your Sneaker Data Collection with Sneaker API

• How to Use LuckData Sneaker API to Get Real-Time Data from Billys Tokyo