Using Python to Scrape Sneaker Data from juicestore.tw

2025-04-08

1. Introduction

In today’s data-driven world, e-commerce platforms often reflect market trends and consumer behavior. juicestore.tw, a website specializing in streetwear and sneakers, offers a wealth of sneaker-related product information—such as names, prices, stock levels, and item codes. The goal of this article is to demonstrate how to use Python to build a web scraper that collects sneaker data from juicestore.tw, providing a valuable dataset for analysis, market research, or inventory monitoring.

We’ll walk through each step of the implementation, discussing the underlying logic and important technical considerations. It’s important to follow ethical scraping practices, including adherence to the site’s robots.txt file and responsible data usage.

2. Preparations

2.1 Tools and Environment

We’ll be using Python for this project because of its extensive third-party libraries and strong community support.

requests: For sending HTTP requests and retrieving page content.
BeautifulSoup (or lxml): For parsing HTML and extracting data.
pandas: For cleaning, transforming, and saving the data.
For advanced scraping needs, tools like Scrapy or aiohttp can improve performance.

Alternatively, if you prefer structured APIs, you can use the Luckdata Sneaker API, which aggregates sneaker data from over 20 platforms including juicestore.tw.

Here’s an example of using the API:

import requests
headers = {
'X-Luckdata-Api-Key': 'your_key'
}
response = requests.get(
'https://luckdata.io/api/sneaker-API/get_peqs?url=https://www.juicestore.tw/products/%E3%80%90salomon%E3%80%91acs-pro-desert-1',
headers=headers
)
data = response.json()
print(data)

2.2 Legal and Ethical Reminder

Check robots.txt: Before scraping, verify that your target URLs are allowed.
Throttle requests: Introduce delays to reduce server load.
Respect data usage: Use the data for educational or personal research purposes only.

3. Data Extraction Workflow

We’ll break the scraping process into four key steps: identifying the target HTML structure, sending requests, parsing the data, and saving the results.

3.1 Identify Target Elements

Using the browser’s Developer Tools (F12), inspect the juicestore.tw site and locate where product data is rendered. Sneaker items are typically inside elements like <div class="sneaker-item">, which contain names and prices.

3.2 Send HTTP Requests

Use the requests library to retrieve HTML from the target URL. Adding headers can help avoid being blocked.

import requests
url = "https://juicestore.tw/sneakers"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
html_content = response.text
print("Page retrieved successfully!")
else:
print("Request failed. Status code:", response.status_code)

3.3 Parse HTML Data

Once we have the HTML, use BeautifulSoup to extract product titles and prices.

from bs4 import BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')
items = soup.find_all("div", class_="sneaker-item")
sneakers_data = []
for item in items:
title = item.find("h2").get_text(strip=True) if item.find("h2") else "No title"
price = item.find("span", class_="price").get_text(strip=True) if item.find("span", class_="price") else "No price"
sneakers_data.append({
"title": title,
"price": price
})
print(sneakers_data)

3.4 Clean and Save the Data

Use pandas to clean the data and export it to a CSV file for further analysis.

import pandas as pd
df = pd.DataFrame(sneakers_data)
df_cleaned = df.dropna()
df_cleaned.to_csv("sneakers_data.csv", index=False)
print("Data saved to sneakers_data.csv")

4. Deeper Exploration and Optimization

Let’s discuss some common real-world scraping issues and how to handle them.

4.1 Paginated Results

If products are listed on multiple pages, iterate through them using a loop.

import time
all_data = []
base_url = "https://juicestore.tw/sneakers?page="
for page in range(1, 6):  # Scrape the first 5 pages
url = base_url + str(page)
response = requests.get(url, headers=headers)
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
items = soup.find_all("div", class_="sneaker-item")
for item in items:
title = item.find("h2").get_text(strip=True) if item.find("h2") else "No title"
price = item.find("span", class_="price").get_text(strip=True) if item.find("span", class_="price") else "No price"
all_data.append({
"title": title,
"price": price
})
time.sleep(2)  # Delay to avoid rate-limiting
df_all = pd.DataFrame(all_data).dropna()
df_all.to_csv("sneakers_all_pages.csv", index=False)
print("All pages saved to sneakers_all_pages.csv")

4.2 Error Handling and Anti-Scraping

You may encounter request failures, timeouts, or anti-bot protections. To address this:

Use try-except blocks to gracefully handle errors.
Rotate proxies if needed.
Randomize delays and headers to mimic real users.

4.3 Asynchronous Scraping

For large-scale scraping, asynchronous tools like aiohttp or Scrapy can speed up the process.

4.4 JavaScript-rendered Content

If content loads dynamically via JavaScript, consider using Selenium or inspect API requests in the browser's network panel.

5. Data Analysis & Visualization (Optional)

The scraped sneaker data can be used for:

Price distribution analysis
Popular product detection
Trend monitoring

Here’s an example of a price histogram using matplotlib:

import matplotlib.pyplot as plt
df_all['price'] = df_all['price'].str.replace(r'\D', '', regex=True).astype(float)
plt.hist(df_all['price'], bins=20)
plt.xlabel("Price")
plt.ylabel("Frequency")
plt.title("Sneaker Price Distribution")
plt.show()

6. Conclusion

This article demonstrated how to build a Python scraper for juicestore.tw to collect sneaker data. From setup and data collection to cleaning and storage, we covered a full scraping workflow. Always respect legal boundaries and site policies.

Future improvements might include:

Advanced data cleaning and enrichment
Predictive analytics or ML models
Real-time scraping dashboards

With practice, you can build efficient and scalable scraping systems that power meaningful data insights.

7. Appendix

Full Code Example

import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
import matplotlib.pyplot as plt
base_url = "https://juicestore.tw/sneakers?page="
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
}
all_data = []
for page in range(1, 6):
url = base_url + str(page)
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
items = soup.find_all("div", class_="sneaker-item")
for item in items:
title = item.find("h2").get_text(strip=True) if item.find("h2") else "No title"
price = item.find("span", class_="price").get_text(strip=True) if item.find("span", class_="price") else "No price"
all_data.append({
"title": title,
"price": price
})
except Exception as e:
print(f"Error on page {page}:", e)
time.sleep(2)
df = pd.DataFrame(all_data).dropna()
df.to_csv("sneakers_all_pages.csv", index=False)
print("Data saved to sneakers_all_pages.csv")
df['price'] = df['price'].str.replace(r'\D', '', regex=True)
df = df[df['price'] != '']
df['price'] = df['price'].astype(float)
plt.hist(df['price'], bins=20)
plt.xlabel("Price")
plt.ylabel("Frequency")
plt.title("Sneaker Price Distribution")
plt.show()