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How to Scrape Walmart Product Data: An In-Depth 2200+ Word Tutorial

Walmart is a retail giant, with over $500 billion in annual revenue and a massive online product catalog. By scraping and analyzing Walmart‘s vast data, businesses can gain valuable insights for everything from pricing research to inventory monitoring.

This 2200+ word guide will teach you how to extract data from Walmart at scale using Python. We‘ll share code snippets, technical how-tos, and critical strategies to avoid getting blocked.

Let‘s dive in!

Why Scrape Walmart Product Data?

First, why would you want to scrape data from Walmart in the first place?

Walmart‘s retail website contains a wealth of information that can inform key business decisions:

  • Pricing intelligence – Track how Walmart and competitors price items over time. This is invaluable for optimizing margins and supply chain costs.
  • Market research – Analyze product demand, customer sentiment, and pricing trends. Useful for product managers and marketing teams.
  • Inventory monitoring – Get alerts for out of stock items or changes in availability. Important for supply chain planning.
  • Ad targeting – Identify seasonally popular items to better target promotions and ads.

These are just a few examples. Walmart‘s data can support a wide range of analytic use cases. The catch? Much of this data is tedious or impossible to collect manually. Scraping provides a scalable way to extract insights.

Before we continue, it‘s important to note that while scraping public Walmart data is generally permitted, there are ethical lines not to cross:

  • Avoid violating Walmart‘s Terms of Use, like aggressively scraping at high volumes.
  • Do not redistribute proprietary data like pricing in bulk.
  • Use data responsibly to inform your internal business decisions rather than undercut Walmart directly.
  • Consider consulting a lawyer about your specific scraping use case.

The bottom line – scrape ethically, limit volume, and avoid directly competing with Walmart using their own data. For more, read our guide on the legality of web scraping.

Step 1: Set Up Python Environment

Let‘s install the packages needed to scrape:

pip install requests beautifulsoup4 pandas

These libraries allow us to send requests (requests), parse HTML (Beautiful Soup), and store data (Pandas).

Then import them:

import requests
from bs4 import BeautifulSoup
import pandas as pd

We‘re ready to start scraping!

Step 2: Find Product URLs

Walmart has thousands of product pages. To scope our scraper, let‘s define a list of URLs to target:

target_urls = [

Let‘s scrape some cleaning supplies, headphones, and a laptop.

Step 3: Analyze Page Structure

We need to understand how Walmart displays product data before extracting it.

The easiest way is using Chrome DevTools:

  • Right click anywhere on a product page and select "Inspect"
  • Click the "Elements" tab
  • Inspect elements to see the HTML structure

For example, to find the product title, we see it‘s in an <h1> tag:

<h1 class="prod-ProductTitle">
  Lysol Disinfecting Wipes, Crisp Linen Scent - 75ct 

And the price is in a <span> with class price:

<span class="price">

Knowing these patterns will help us locate data with BeautifulSoup.

Step 4: Scrape Page Content

To download the page content, we‘ll use the Requests library:

import requests

for url in target_urls:

  response = requests.get(url)

  page_html = response.text

This sends a GET request to each URL and stores the HTML content.

Step 5: Parse HTML with BeautifulSoup

Next, we can parse the page HTML using BeautifulSoup:

from bs4 import BeautifulSoup

soup = BeautifulSoup(page_html, ‘html.parser‘)

BeautifulSoup takes the page text and creates a browsable DOM tree.

Step 6: Extract Product Data

With our soup object ready, we can extract elements by CSS class and other patterns we identified:


title = soup.find(‘h1‘, class_=‘prod-ProductTitle‘).text


price = soup.find(‘span‘, class_=‘price‘).text.strip()

Image URL:

img_url = soup.find(‘img‘, class_=‘product-image‘)[‘src‘]

And so on for other fields like product descriptions, ratings, etc.

Step 7: Store in Data Structures

Let‘s store the results in a Python dictionary:

product_data = {
  ‘title‘: title,
  ‘price‘: price,
  ‘img_url‘: img_url

We can then append these to a list:

all_data = []

# Scrape each page
for url in target_urls:

  # Scraping logic

  product_data = {‘title‘: ..., ‘price‘: ...}


Step 8: Export to CSV

Finally, we can convert our list of dicts to a Pandas DataFrame and export to CSV:

import pandas as pd

df = pd.DataFrame(all_data)

df.to_csv(‘scraped_data.csv‘, index=False)

This saves a structured CSV file containing the scraped data!

Avoiding Blocks

Walmart implements protections against scraping bots. Here are some tips to avoid blocks:

Rotate Proxies

Using different proxy IPs prevents your main IP from getting flagged for suspicious traffic.

Randomize User Agents

Mimic different devices by changing the user agent with each request:

from random import choice

user_agents = [

headers = {‘User-Agent‘: choice(user_agents)}

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

Add Random Delays

Space out requests instead of slamming the server:

from time import sleep
from random import randrange

# Add random delay
sleep(randrange(2, 5)) 

This mimics human browsing patterns.

Expanding the Scraper

Here are some ways to build on our basic scraper:

  • Extract additional fields like ratings, reviews, etc.
  • Iterate through multiple search result pages
  • Scrape related and "Customers also viewed" items
  • Run on a schedule to collect daily pricing data
  • Monitor inventory status with alerts for out of stock items

The possibilities are endless!

Full Python Script

Now that we‘ve covered each step, here is a full Python script putting it all together:

# Imports
import requests
from bs4 import BeautifulSoup
import pandas as pd

# Target URLs
target_urls = [

# Initialize empty list    
all_data = [] 

# Loop over URLs
for url in target_urls:

  # Get page HTML
  response = requests.get(url)
  html = response.text

  # Parse HTML
  soup = BeautifulSoup(html, ‘html.parser‘)

  # Extract data
  title = soup.find(‘h1‘, class_=‘prod-ProductTitle‘).text
  price = soup.find(‘span‘, class_=‘price‘).text.strip()

  # Store data 
  product_data = {‘title‘: title, ‘price‘: price}

# Convert to DataFrame  
df = pd.DataFrame(all_data)

# Export CSV
df.to_csv(‘scraped_data.csv‘, index=False)

This scraper extracts the title and price from a list of product URLs. You can add on more complexity like proxies, user agents, delays etc.


Scraping enables you to extract insights from Walmart‘s data at scale. This guide covered core concepts like:

  • Using Requests and BeautifulSoup to scrape product pages
  • Parsing HTML elements
  • Storing and exporting scraped data
  • Avoiding blocks with proxies and delays

The methods can be adapted to build scrapers for any retail site.

Happy scraping! Scrap ethically, stay within site guidelines, and use data responsibly.


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