AliExpress Product Data Extraction: Prices, Reviews, and Sellers with Python

AliExpress is one of the world's largest e-commerce platforms, with millions of products from Chinese sellers. Extracting product data — prices, reviews, seller ratings — is invaluable for e-commerce intelligence, price monitoring, and market research.

In this guide, I'll walk you through building an AliExpress scraper with Python.

Use Cases for AliExpress Data

• Price comparison: Track product prices across sellers
• Dropshipping research: Find profitable products with good margins
• Competitor monitoring: Watch competitor pricing strategies
• Review sentiment analysis: Understand product quality from buyer feedback
• Supplier evaluation: Compare seller ratings and reliability

Setting Up

import requests
from bs4 import BeautifulSoup
import json
import re
import time

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36",
    "Accept-Language": "en-US,en;q=0.9",
}

# ScraperAPI handles proxy rotation and JS rendering
SCRAPER_API_KEY = "YOUR_KEY"

For handling AliExpress's anti-bot protections, ScraperAPI is excellent — it manages proxy rotation and browser rendering automatically.

Extracting Product Details

AliExpress heavily uses JavaScript rendering. Here's how to extract product data:

def scrape_product(product_url):
    """Extract details from an AliExpress product page."""
    api_url = f"http://api.scraperapi.com?api_key={SCRAPER_API_KEY}&url={product_url}&render=true"

    response = requests.get(api_url, timeout=60)
    soup = BeautifulSoup(response.text, "html.parser")

    # AliExpress stores product data in window.runParams
    script_tags = soup.find_all("script")
    product_data = {}

    for script in script_tags:
        if script.string and "window.runParams" in script.string:
            match = re.search(r'data:\s*(\{.*?\})\s*;', script.string, re.DOTALL)
            if match:
                try:
                    product_data = json.loads(match.group(1))
                except json.JSONDecodeError:
                    continue

    return parse_product_data(product_data)


def parse_product_data(data):
    """Parse structured product information."""
    price_info = data.get("priceModule", {})
    title_info = data.get("titleModule", {})
    seller_info = data.get("storeModule", {})
    feedback = data.get("feedbackModule", {})

    return {
        "title": title_info.get("subject", ""),
        "price": price_info.get("formatedActivityPrice",
                  price_info.get("formatedPrice", "")),
        "original_price": price_info.get("formatedPrice", ""),
        "currency": price_info.get("currencyCode", "USD"),
        "orders": title_info.get("tradeCount", 0),
        "rating": feedback.get("averageStar", 0),
        "reviews_count": feedback.get("totalValidNum", 0),
        "store_name": seller_info.get("storeName", ""),
        "store_rating": seller_info.get("positiveRate", ""),
        "store_followers": seller_info.get("followingNumber", 0),
    }

Scraping Search Results

def scrape_search_results(query, pages=3):
    """Search AliExpress and extract product listings."""
    all_products = []

    for page in range(1, pages + 1):
        search_url = f"https://www.aliexpress.com/wholesale?SearchText={query}&page={page}"
        api_url = f"http://api.scraperapi.com?api_key={SCRAPER_API_KEY}&url={search_url}&render=true"

        response = requests.get(api_url, timeout=60)
        soup = BeautifulSoup(response.text, "html.parser")

        # Find product cards
        items = soup.select("div[class*='search-item-card']")

        for item in items:
            product = {
                "title": item.select_one("h1, h3, [class*='title']"),
                "price": item.select_one("[class*='price']"),
                "orders": item.select_one("[class*='trade']"),
                "rating": item.select_one("[class*='evaluation']"),
                "link": item.select_one("a[href*='/item/']"),
            }

            all_products.append({
                "title": product["title"].text.strip() if product["title"] else "",
                "price": product["price"].text.strip() if product["price"] else "",
                "orders": product["orders"].text.strip() if product["orders"] else "",
                "rating": product["rating"].text.strip() if product["rating"] else "",
                "url": "https:" + product["link"]["href"] if product["link"] else "",
            })

        print(f"Page {page}: Found {len(items)} products")
        time.sleep(3)

    return all_products

Extracting Reviews

def scrape_reviews(product_id, pages=5):
    """Extract product reviews."""
    reviews = []

    for page in range(1, pages + 1):
        review_url = (
            f"https://feedback.aliexpress.com/display/productEvaluation.htm"
            f"?productId={product_id}&page={page}"
        )
        api_url = f"http://api.scraperapi.com?api_key={SCRAPER_API_KEY}&url={review_url}"

        response = requests.get(api_url, timeout=30)
        soup = BeautifulSoup(response.text, "html.parser")

        review_items = soup.select(".feedback-item")
        for item in review_items:
            reviews.append({
                "rating": len(item.select(".star-view .star-active")),
                "text": item.select_one(".buyer-feedback span").text.strip()
                        if item.select_one(".buyer-feedback span") else "",
                "date": item.select_one(".r-time").text.strip()
                        if item.select_one(".r-time") else "",
                "country": item.select_one(".user-country").text.strip()
                           if item.select_one(".user-country") else "",
            })

        time.sleep(2)

    return reviews

The Production-Ready Approach

Building a robust AliExpress scraper is challenging due to frequent page structure changes and aggressive anti-bot measures. For reliable, large-scale extraction, check out the AliExpress Scraper on Apify — it handles all the rendering, captcha solving, and data parsing out of the box.

Saving Data

import pandas as pd

def save_products(products, filename="aliexpress_products.csv"):
    df = pd.DataFrame(products)
    df.to_csv(filename, index=False)
    print(f"Saved {len(products)} products to {filename}")

# Example usage
products = scrape_search_results("wireless earbuds", pages=5)
save_products(products)

Tips for Reliable AliExpress Scraping

1. Use a rendering service: AliExpress relies heavily on JavaScript. ScraperAPI handles this automatically
2. Rotate User-Agents: Vary your browser fingerprint
3. Respect rate limits: 2-5 seconds between requests minimum
4. Handle currency/locale: Set appropriate cookies for consistent pricing data
5. Monitor for changes: AliExpress updates their frontend frequently

Conclusion

AliExpress product data extraction is powerful for e-commerce intelligence, but requires handling JavaScript rendering and anti-bot protections. Whether you build a custom solution or use a managed tool like the AliExpress Scraper, always scrape responsibly.

Happy data mining!