How to Scrape Walmart Product Data Like a Pro

Every day, Walmart hosts millions of products, with prices and reviews changing constantly. Imagine having a system that can track all of that automatically. That’s the magic of web scraping. And yes, you can do it efficiently with Python.
In this guide, we’ll walk you through scraping Walmart product pages—prices, reviews, SKUs, and more—while avoiding anti-bot roadblocks. You’ll learn practical, actionable steps, from setting up tools to saving your data for analysis. Let’s jump in.

What Web Scraping Is Crucial

Web scraping involves using code to automatically collect data from a webpage. For Walmart, this might include monitoring daily price changes, compiling reviews for analysis, or creating external databases for research and insights.
While the concept seems straightforward, Walmart’s anti-bot measures add complexity. The success of your scraping efforts depends on your strategy and execution, influencing whether you gather useful data or face repeated CAPTCHAs.

Requirements

Before scraping, get your Python environment ready. Essential libraries:

• Requests: send HTTP requests
• BeautifulSoup: parse HTML content
• Selenium: handle JavaScript-heavy pages
• Json: extract structured data

Install everything in one command:

pip install requests beautifulsoup4 selenium

Next, open a Walmart product page in Chrome or Firefox, right-click → Inspect → locate <script> tags. That’s where structured JSON lives—the goldmine of product data.

Getting Product URLs and SKUs

Each Walmart product includes a distinct ID within its URL or embedded in the page’s script tags. SKUs are typically found close to <span> elements marked “SKU.”
Once you identify the product ID and locate the corresponding JSON in the browser’s developer tools, you can proceed with scraping.

Constructing the Python Scraper

Here’s how to extract name, price, and reviews step by step.

Step 1: Import Libraries

import requests
from bs4 import BeautifulSoup
import json

Step 2: Set URL and Headers

url = "https://www.walmart.com/ip/123456789"
headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}

Step 3: Send GET Request

response = requests.get(url, headers=headers)
print(response.status_code)

• 200? Perfect.
• Other codes? Likely an IP block or CAPTCHA.

Step 4: Parse HTML and Extract JSON

soup = BeautifulSoup(response.text, "html.parser")
script = soup.find("script", {"type": "application/ld+json"})
data = json.loads(script.string)

Step 5: Grab Product Info

print("Name:", data["name"])
print("Price:", data["offers"]["price"])
print("Rating:", data["aggregateRating"]["ratingValue"])

Boom. You’ve got the core product info.

Handling Anti-Bot Measures With Selenium

Some pages can be difficult to scrape, but Selenium helps by simulating a real browser, handling dynamic content, and bypassing JavaScript blockers.
For optimal results, use headless mode when scraping at scale, rotate proxies to avoid IP bans, and introduce delays to mimic human browsing. These strategies make working with Walmart much more manageable.

Storing the Data

Export your scraped data for analysis.

CSV Example

import csv
with open("output.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["product id","name","price"])
    writer.writerow([pid, name, price])

JSON Example

with open("output.json", "w") as f:
    json.dump(data, f)

These files make it easy to track trends, feed APIs, or run analysis later.

Is It Permissible to Scrape Walmart

Scraping Walmart is permissible if done responsibly. Focus on publicly available data and steer clear of content behind logins, while adhering to copyright regulations. Be sure to consult Walmart’s robots.txt, use a legitimate user-agent, and space out your requests to avoid overwhelming the server. Employing proxies and rate limiting can further help keep your scraping safe and efficient.

Conclusion

Scraping Walmart is challenging but doable. Use real user-agent headers, respect rate limits and robots.txt, combine JSON-LD with CSS selectors, and rely on Selenium and proxies for tricky pages. Mastering these turns Walmart’s data into a valuable resource.