How to Collect Momentum Data Using Python: A Comprehensive Guide on Crawlers, API, and Proxy Applications

2025-03-24

In the digital age, the sneaker market has seen rapid growth, and as a well-known e-commerce platform for sneakers in Taiwan, Momentum offers a diverse range of products. Its website data has become an essential reference in the industry.

1. Overview of Data Collection Methods

When collecting Momentum data, the following methods are commonly used:

1.1 Web Crawlers

Traditional crawlers simulate browser requests to fetch the HTML content and extract the required data. Common Python libraries for this approach include:

requests: For sending HTTP requests.
BeautifulSoup or lxml: For parsing HTML documents.
Selenium: For handling JavaScript-rendered pages.

When using crawlers, it is essential to consider anti-scraping mechanisms, such as checking robots.txt, simulating browser headers, and introducing proxy IPs to avoid being blocked by the website.

1.2 API Interface Access

Accessing data through an API is more efficient and stable. Some data from Momentum can be obtained via API, and LuckData’s Sneaker API provides an integrated interface for multiple sneaker websites, including Momentum. The advantages of this API include:

Directly returning data in JSON format, avoiding the need to parse HTML.
Cross-platform data access, facilitating data integration.
Different subscription plans (Free, Basic, Pro, Ultra) cater to different user needs and request frequencies.

1.3 Direct Access and Other Methods

Besides crawlers and APIs, you can use browser automation tools or ready-made data platforms to collect data from websites. These methods are often suitable for simpler or one-time data extraction tasks.

2. A Detailed Guide to Collecting Momentum Data Using Python Crawlers

2.1 Using requests and BeautifulSoup to Scrape Static Page Data

Here’s a basic example of how to use requests and BeautifulSoup to scrape basic product information from Momentum’s website:

import requests
from bs4 import BeautifulSoup
# Simulating a browser request header
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36"
}
# Set the target product URL
url = "https://www.momentum.com.tw/products/A07611C"
response = requests.get(url, headers=headers)
if response.status_code == 200:
soup = BeautifulSoup(response.text, "html.parser")
# Adjust the selector based on the actual webpage structure; this is just an example
product_title = soup.find("h2", class_="product-title").text.strip() if soup.find("h2", class_="product-title") else "Product name not found"
product_price = soup.find("span", class_="price").text.strip() if soup.find("span", class_="price") else "Price not found"
print("Product Name:", product_title)
print("Product Price:", product_price)
else:
print("Request failed with status code:", response.status_code)

2.2 Using Selenium for Dynamic Page Rendering

For pages that load data dynamically through JavaScript, Selenium can simulate browser interactions, waiting for the page to load before extracting the data:

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
# Set up ChromeDriver to run in headless mode
options = webdriver.ChromeOptions()
options.add_argument("--headless")
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
driver.get("https://www.momentum.com.tw/products/A07611C")
driver.implicitly_wait(5)  # Wait for the page to load
try:
title_element = driver.find_element(By.XPATH, "//h2[@class='product-title']")
price_element = driver.find_element(By.XPATH, "//span[@class='price']")
print("Product Name:", title_element.text)
print("Product Price:", price_element.text)
except Exception as e:
print("Error in data extraction:", e)
driver.quit()

2.3 Considerations and Anti-Scraping Strategies

Using Proxy IPs: If you find your IP being blocked by the target website or encounter frequent request issues, proxies can be used to solve this. LuckData’s proxy products (including data center proxies and dynamic residential proxies) can effectively address this issue, providing stable requests and geographical location support.
Request Frequency Control: Set reasonable delays between requests to reduce the risk of being blocked.

3. Using LuckData Sneaker API to Retrieve Momentum Data

3.1 Introduction to LuckData Sneaker API

The LuckData Sneaker API is a powerful tool that integrates multiple sneaker website APIs, designed for developers and sneaker enthusiasts. Not only does it support Momentum data interfaces, but it also integrates other popular platforms (such as Footlocker, Musinsa, etc.). Users can choose from different subscription plans based on their needs, ranging from Free to Ultra, each offering various benefits.

3.2 How to Call the LuckData API Using Python

With LuckData’s API, you can easily obtain Momentum product data. Here’s how to call the API in Python:

import requests
# Set the API Key
API_KEY = "your_key"
# Set the target product URL
product_url = "https://www.momentum.com.tw/products/A07611C"
# API request URL
api_url = f"https://luckdata.io/api/sneaker-API/get_9492?url={product_url}"
# Request headers
headers = {
"X-Luckdata-Api-Key": API_KEY
}
# Send GET request
response = requests.get(api_url, headers=headers)
if response.status_code == 200:
data = response.json()
print("Product Information:", data)
else:
print("Request failed with status code:", response.status_code)

This method allows you to directly obtain structured JSON data, avoiding the hassle of HTML parsing. The API data is updated in real time, making it suitable for advanced applications such as bulk data retrieval and database storage.

3.3 Advanced Usage and Data Storage

After obtaining data from the LuckData API, you can store it in a database such as MySQL or MongoDB based on your needs. This approach makes it easier to query the data later and helps in building a data analytics system. Advanced usage also involves adjusting API request frequencies, implementing retry mechanisms, and optimizing data fetching processes.

4. Optimizing Data Collection with Proxy Products

4.1 Why Use Proxies

In data scraping and API request processes, proxy IPs help:

Bypass IP restrictions and geographical blocks;
Hide real IPs, ensuring privacy protection;
Improve request stability, preventing blocks and traffic limits.

LuckData’s proxy products, including data center proxies, dynamic residential proxies, and unlimited dynamic residential proxies, meet various application scenarios, offering high speed, stability, and security.

4.2 Example of Using LuckData Proxies in Python

Here’s an example of using LuckData’s proxy products for network requests:

import requests
# Proxy address format: http://Account:Password@ahk.luckdata.io:Port
proxyip = "http://Account:Password@ahk.luckdata.io:Port"
url = "https://api.ip.cc"
proxies = {
'http': proxyip,
'https': proxyip,
}
response = requests.get(url, proxies=proxies)
print(response.text)

With this configuration, whether using a crawler or making API requests, you can more reliably retrieve data without encountering IP block issues.

5. Conclusion and Future Prospects

This article provides a detailed guide on how to collect Momentum data using Python, covering various methods such as traditional crawlers, API calls, and direct access. It also incorporates LuckData’s products to demonstrate how proxy technologies can improve data collection stability. Each method has its own advantages and disadvantages:

Crawlers are suitable for scraping static or dynamic pages but require attention to anti-scraping and IP blocking issues;
API interfaces offer faster, structured data access, making them ideal for bulk data handling and advanced applications;
Proxy products help ensure continuous and stable data retrieval.

As the market and technologies evolve, developers can choose the appropriate method based on their specific needs and continue to stay updated with the latest tools and techniques.