Implementing and Reflecting on Sneaker Data Extraction from Crazy11

2025-04-09

In the era of big data, information has become a crucial asset for decision-making across various industries. This article provides a detailed walkthrough of a project focused on extracting sneaker data from the Crazy11 website. It covers project motivation, technical implementation, challenges encountered, and legal/ethical considerations. The goal is to summarize reusable practices and offer a reference for similar data collection projects.

2. Project Background and Motivation

1. Introduction to Crazy11

Crazy11 is a platform that focuses on sports gear and trendy sneaker data. It provides extensive details on various sneaker models, including names, prices, release dates, and user reviews. For sneaker enthusiasts and market analysts, such data is valuable for tracking trends, monitoring price fluctuations, and analyzing product popularity.

2. Significance of Data Extraction

With the rapid growth of e-commerce and sneaker culture, timely access to market information is crucial. Web scraping technology enables the efficient collection of large volumes of sneaker data, which can support trend analysis, consumer behavior studies, and competitive landscape assessments. This project also serves as a practical exercise for those learning web scraping and data cleaning techniques.

3. Legal and Ethical Considerations

1. Legality and Website Policy

Before starting any scraping activity, it’s essential to review Crazy11’s Terms of Use and Privacy Policy. Unauthorized data extraction may violate copyright laws or site rules, posing legal risks. Comprehensive research is needed to ensure the process is compliant with all regulations.

2. Ethical Practice

Responsible data extraction should minimize server impact. This project incorporates polite scraping practices such as setting request intervals and honoring the site’s robots.txt file. Ethical scraping not only shows respect for the website but also helps maintain a healthy web ecosystem.

4. Technical Approach and Tools

1. Programming Language and Libraries

This project uses Python, supported by the following libraries:

Requests: For sending HTTP requests and retrieving web content.
BeautifulSoup: For parsing HTML and extracting structured data.
Scrapy (optional): Ideal for large-scale scraping with built-in support for pipelines and distributed systems.

2. Development Environment Setup

Install Python 3.

Install required libraries:

pip install requests pip install beautifulsoup4

Use IDEs like VS Code or PyCharm to structure and write the code.

3. Overall Scraping Workflow

The main steps in the data extraction process include:

Source Analysis: Identify the structure of Crazy11’s web pages and locate sneaker-related elements or API endpoints.
Request Handling: Simulate browser behavior with appropriate headers, cookies, and user agents.
Data Parsing: Use BeautifulSoup or XPath to extract target information.
Data Storage: Save the results to CSV or a database for persistence.
Error Handling: Manage request failures and missing data to ensure stability.

5. Scraping Design and Implementation

1. Source Analysis

Using browser developer tools, we observe that each sneaker listing is wrapped in a div with sub-elements such as model name, price, and release date. This allows us to define precise scraping rules.

2. Request Simulation and Data Extraction

Sample Python scraping code:

import requests
from bs4 import BeautifulSoup
import csv
import time
url = "https://crazy11.co.kr/sneakers"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
}
def fetch_data(page_url):
try:
response = requests.get(page_url, headers=headers, timeout=10)
response.raise_for_status()
return response.text
except requests.RequestException as e:
print(f"Request failed: {e}")
return None
def parse_data(html):
soup = BeautifulSoup(html, "html.parser")
sneakers = []
for item in soup.find_all("div", class_="sneaker-item"):
title = item.find("h2", class_="sneaker-title").get_text(strip=True)
price = item.find("span", class_="sneaker-price").get_text(strip=True)
release_date = item.find("span", class_="release-date").get_text(strip=True)
sneakers.append({
"title": title,
"price": price,
"release_date": release_date
})
return sneakers
def save_to_csv(data, filename="sneakers_data.csv"):
keys = data[0].keys() if data else []
with open(filename, "w", newline="", encoding="utf-8") as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=keys)
writer.writeheader()
writer.writerows(data)
print(f"Data saved to {filename}")
if __name__ == "__main__":
html_content = fetch_data(url)
if html_content:
sneaker_data = parse_data(html_content)
if sneaker_data:
save_to_csv(sneaker_data)
time.sleep(2)

For real-world use, consider adding pagination, proxy support, and retry logic to improve performance and robustness.

3. Data Storage Options

The default output format is CSV, which is suitable for basic analysis. For larger datasets, consider using SQLite or MySQL with a structured schema (e.g., id, title, price, release_date) to support efficient querying and downstream analytics.

4. Using an API to Retrieve Data (Recommended)

Compared to scraping, APIs offer more reliable and efficient data access. Crazy11 data can be accessed through the Luckdata Sneaker API:

import requests
headers = {
'X-Luckdata-Api-Key': 'your_key'
}
response = requests.get(
'/api/sneaker-API/get_get_yg6d?url=https://www.crazy11.co.kr//shop/shopdetail.html?branduid=806352&xcode=070&mcode=010&scode=&type=Y&sort=order&cur_code=070010&search=&GfDT=bmp7W10%3D',
headers=headers
)
print(response.json())

The Luckdata API supports multiple platforms like Crazy11, Footlocker, Musinsa, and Kasina, making it ideal for cross-platform sneaker analytics.

6. Data Cleaning and Preprocessing

Raw data may contain duplicates, missing values, or inconsistent formats. Cleaning involves:

import pandas as pd
df = pd.read_csv("sneakers_data.csv")
# Remove duplicates
df.drop_duplicates(inplace=True)
# Normalize price format
df["price"] = df["price"].str.replace("₩", "").astype(float)
# Convert release date to datetime
df["release_date"] = pd.to_datetime(df["release_date"], errors='coerce')
# Check for missing values
print(df.isnull().sum())
# Save cleaned data
df.to_csv("sneakers_data_clean.csv", index=False)

Cleaned data is more suitable for modeling, analytics, and visualization.

7. Data Analysis and Applications

Typical use cases include:

Price Distribution: Analyze sneaker counts across price segments.
Brand Popularity: Track the number of releases by brand and market share.
Release Trends: Use time series analysis to discover peak seasons and launch cycles.

Visualization tools like Matplotlib and Seaborn can be used to support insights and business decisions.

8. Challenges and Solutions

Common issues and how they were addressed:

Anti-Scraping Measures: Bypassed with headers, delays, and rotating proxies.
Dynamic Content: Solved using Selenium or backend API endpoints.
Inconsistent Data: Handled via preprocessing and standardization.
API Limitations: Free API plans have restrictions; consider upgrading for heavy usage.

These strategies ensured project stability and provide lessons for future endeavors.

9. Conclusion and Outlook

This project successfully achieved its goal of extracting sneaker data from Crazy11 while generating key insights and reusable knowledge:

Technical Execution: Choosing the right tools and tactics is essential for efficient scraping.
Compliance Awareness: Understanding and adhering to site policies reduces legal and ethical risks.
Practical Value: Cleaned data can support commercial analysis, research, and trend forecasting.

Looking ahead, future work could explore distributed scraping, integrate machine learning for trend prediction, or build dashboards for real-time analytics. This project serves as a useful reference for developers and data enthusiasts seeking to harness web data for meaningful insights.