A Complete Guide to Scraping Walmart Product Reviews Using Python

In e-commerce data analysis, obtaining user reviews is crucial for product research, market analysis, and sentiment analysis. Walmart, as a global retail giant, provides valuable review data that can be leveraged for various purposes.

This article introduces two effective methods to collect Walmart product reviews:

1. Traditional Web Scraping: Using requests and BeautifulSoup to parse Walmart’s web pages.

2. LuckData API: Using LuckData API to retrieve structured Walmart review data in a more stable and efficient manner.

Additionally, we will share important knowledge regarding data scraping, such as anti-scraping mechanisms and data storage strategies, to help you better manage and analyze the retrieved data.

1. Scraping Walmart Reviews Using Web Scraping

A basic way to retrieve Walmart reviews is by scraping the product page directly and extracting the review data. This method works for all websites but requires handling anti-scraping mechanisms.

1.1 Prerequisites

Before getting started, make sure you have installed the following Python libraries:

pip install requests beautifulsoup4

1.2 Scraping Walmart Product Reviews

import requests
from bs4 import BeautifulSoup
# Walmart product review page URL (replace with actual URL)
url = 'https://www.walmart.com/product/reviews/your-product-id'
# Set headers to simulate a browser visit
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'
}
response = requests.get(url, headers=headers)
# Check if request was successful
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
# Find all review sections
reviews = soup.find_all('div', {'class': 'review'})
for review in reviews:
comment = review.find('div', {'class': 'review-text'}).text.strip()
rating = review.find('span', {'class': 'stars-container'}).text.strip()
print(f'Rating: {rating}')
print(f'Review: {comment}')
print('-' * 40)
else:
print(f"Request failed, status code: {response.status_code}")

1.3 Handling Walmart’s Anti-Scraping Mechanisms

Walmart may implement anti-scraping measures that block requests. Here are some strategies to improve scraping success:

• Modify User-Agent: Mimic different browsers to reduce the chances of being blocked.

• Use Proxy IPs: Avoid getting blocked due to frequent requests from the same IP.

• Use Selenium for Dynamic Content: If review data is loaded via JavaScript, use Selenium to extract the dynamic content.

2. Using LuckData API to Retrieve Walmart Reviews

If you prefer not to parse HTML manually and want a more stable and efficient solution, LuckData API allows direct access to Walmart product reviews.

2.1 Introduction to LuckData Walmart API

LuckData provides a Walmart API that enables developers to access Walmart’s product catalog, detailed information, and reviews without dealing with web scraping challenges.

2.2 LuckData API Pricing

LuckData API offers four pricing tiers, allowing users to choose based on their request volume and speed requirements:

All plans provide full data access, differing only in the number of requests and speed limits.

2.3 Example: Fetching Walmart Reviews via LuckData API

import requests
# Set your LuckData API Key
api_key = 'your-luckdata-key'  # Replace with your actual LuckData API Key
# Walmart product URL and SKU ID
product_url = 'https://www.walmart.com/ip/example-product'  # Replace with actual Walmart product link
sku_id = '123456789'  # Replace with actual SKU ID
# Set request headers
headers = {
'X-Luckdata-Api-Key': api_key
}
# Construct API request URL
api_url = f'https://luckdata.io/api/walmart-API/get_v1me?url={product_url}&sku={sku_id}&page=1'
# Send request
response = requests.get(api_url, headers=headers)
# Parse returned JSON data
if response.status_code == 200:
data = response.json()
print("Retrieved Data:", data)
if 'reviews' in data:
for review in data['reviews']:
print(f"User: {review.get('user', 'Anonymous')}")
print(f"Rating: {review.get('rating', 'N/A')}")
print(f"Review: {review.get('comment', 'No Review')}")
print('-' * 40)
else:
print("No review data found")
else:
print(f"Request failed, status code: {response.status_code}, error message: {response.text}")

2.4 Why Choose LuckData API?

Compared to traditional scraping, LuckData API offers several advantages:

• No need to handle anti-scraping mechanisms, ensuring stable requests without website changes affecting your data retrieval.

• Returns structured JSON data, eliminating the need for HTML parsing.

• Supports multiple platforms, including Amazon, Google, TikTok, and thousands of other e-commerce platforms.

• Scalable and flexible, allowing businesses and developers to retrieve large-scale data efficiently.

• No infrastructure management required, making it a hassle-free solution for real-time data extraction.

3. Advanced Data Processing Techniques

3.1 Storing Review Data in CSV

import csv
# Save review data to a CSV file
def save_to_csv(reviews, filename="walmart_reviews.csv"):
with open(filename, mode='w', newline='', encoding='utf-8') as file:
writer = csv.writer(file)
writer.writerow(["User", "Rating", "Review"])
for review in reviews:
writer.writerow([review["user"], review["rating"], review["comment"]])

3.2 Text Cleaning for Reviews

import re
def clean_text(text):
text = re.sub(r'<.*?>', '', text)  # Remove HTML tags
text = text.strip()
return text

4. Conclusion

This article introduced two effective methods to collect Walmart product reviews:

1. Web Scraping is suitable for personal research but requires handling anti-scraping mechanisms.

2. LuckData API provides a stable, fast, and structured data interface, making it an ideal choice for business applications.

If you need fast and reliable review data, LuckData API is the best option : https://luckdata.io/marketplace/detail/walmart-API