Best Walmart deals to compete with Amazon's Big Spring Sale 2026

Learn to build a web scraper that monitors Walmart product prices to identify the best deals, particularly for Apple products and Roku devices mentioned in the news about Walmart's competition with Amazon's Big Spring Sale 2026.

Introduction

In the ongoing battle between Amazon and Walmart for holiday shopping dominance, Walmart is stepping up its game with impressive deals that could rival Amazon's Big Spring Sale 2026. This tutorial will teach you how to build a web scraper that monitors Walmart's product listings and price changes to help you identify the best deals, particularly focusing on Apple products and Roku devices mentioned in the news.

By the end of this tutorial, you'll have a working Python script that monitors Walmart product pages and alerts you to significant price drops or special offers, helping you time your purchases for maximum savings.

Prerequisites

To follow this tutorial, you'll need:

Python 3.7 or higher installed on your system
Basic understanding of Python programming concepts
Knowledge of web scraping concepts
Access to a terminal or command prompt

Step-by-Step Instructions

1. Set Up Your Development Environment

First, create a new directory for our project and set up a virtual environment to keep our dependencies isolated:

mkdir walmart_deal_scraper
 cd walmart_deal_scraper
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Why: Creating a virtual environment ensures that our project dependencies don't interfere with other Python projects on your system.

2. Install Required Dependencies

Install the necessary Python packages for web scraping and data handling:

pip install requests beautifulsoup4 pandas schedule

Why: These packages provide the core functionality for making HTTP requests, parsing HTML content, handling data structures, and scheduling our scraping tasks.

3. Create the Main Scraper Script

Create a file named walmart_scraper.py and add the following code:

import requests
from bs4 import BeautifulSoup
import time
import pandas as pd
import schedule
import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Walmart product URLs to monitor
PRODUCTS = [
    {
        'name': 'Apple iPhone 14',
        'url': 'https://www.walmart.com/ip/Apple-iPhone-14-128GB-Black/123456789',
        'target_price': 999
    },
    {
        'name': 'Roku Streaming Stick+',
        'url': 'https://www.walmart.com/ip/Roku-Streaming-Stick-Plus/987654321',
        'target_price': 79
    }
]

# Headers to mimic a real browser
HEADERS = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
    'Accept-Language': 'en-US,en;q=0.5',
    'Accept-Encoding': 'gzip, deflate',
    'Connection': 'keep-alive',
}

def get_product_price(url):
    """Extract product price from Walmart page"""
    try:
        response = requests.get(url, headers=HEADERS)
        response.raise_for_status()
        soup = BeautifulSoup(response.content, 'html.parser')
        
        # Look for price elements (this selector may need adjustment based on Walmart's current HTML)
        price_element = soup.find('span', {'data-testid': 'price-current'})
        if not price_element:
            price_element = soup.find('span', {'class': 'price-current'})
        
        if price_element:
            price_text = price_element.get_text().strip()
            # Extract numeric value from price text
            price = float(''.join(filter(str.isdigit, price_text)))
            return price
        return None
    except Exception as e:
        logging.error(f"Error scraping {url}: {str(e)}")
        return None

def check_deals():
    """Check all monitored products for deals"""
    deals = []
    
    for product in PRODUCTS:
        current_price = get_product_price(product['url'])
        
        if current_price and current_price <= product['target_price']:
            discount = ((product['target_price'] - current_price) / product['target_price']) * 100
            deals.append({
                'product': product['name'],
                'current_price': current_price,
                'target_price': product['target_price'],
                'discount': round(discount, 2),
                'url': product['url']
            })
            
            logging.info(f"DEAL FOUND: {product['name']} - Current: ${current_price}, Target: ${product['target_price']} ({discount:.2f}% off)")
        
        time.sleep(2)  # Be respectful to the server
    
    if deals:
        # Save deals to CSV
        df = pd.DataFrame(deals)
        df.to_csv('deals_found.csv', index=False)
        logging.info("Deals saved to deals_found.csv")
    else:
        logging.info("No deals found at this time")

# Schedule the check to run every 2 hours
schedule.every(2).hours.do(check_deals)

if __name__ == '__main__':
    logging.info("Starting Walmart Deal Scraper...")
    check_deals()  # Run immediately
    
    while True:
        schedule.run_pending()
        time.sleep(60)

Why: This script sets up a foundation for monitoring product prices, with proper error handling and logging to track what's happening during scraping.

4. Configure Product Monitoring

Update the PRODUCTS list in your script with actual Walmart product URLs. You can find these by:

Visiting Walmart.com
Searching for products like Apple iPhones or Roku devices
Copying the URL from the product page

Replace the placeholder URLs in the script with real product URLs. The script will check for price drops below your specified target prices.

5. Run the Scraper

Execute your scraper script:

python walmart_scraper.py

Why: This will start the monitoring process and check for deals every 2 hours, saving any found deals to a CSV file for easy review.

6. Analyze Results

After running the scraper for a while, you'll have a deals_found.csv file containing all the deals that met your criteria. The CSV will include:

Product name
Current price
Target price
Discount percentage
Product URL

Review this file to identify the best deals available for your shopping.

7. Enhance the Scraper

For more advanced functionality, consider adding:

# Add email notifications for deals
import smtplib
from email.mime.text import MIMEText

# Function to send email alerts
def send_email_alert(deals):
    # Email configuration
    smtp_server = "smtp.gmail.com"
    port = 587
    sender_email = "[email protected]"
    password = "your_password"
    
    message = MIMEText(f"New deals found:\n{deals}")
    message["Subject"] = "Walmart Deal Alert!"
    message["From"] = sender_email
    message["To"] = "[email protected]"
    
    # Send email
    with smtplib.SMTP(smtp_server, port) as server:
        server.starttls()
        server.login(sender_email, password)
        server.sendmail(sender_email, "[email protected]", message.as_string())

Why: Adding email notifications ensures you don't miss time-sensitive deals even when you're not actively monitoring your computer.

Summary

This tutorial demonstrated how to build a Walmart deal monitoring system that can help you identify the best deals, particularly for Apple products and Roku devices mentioned in the news about Walmart's competition with Amazon's Big Spring Sale 2026. You learned how to:

Set up a Python web scraping environment
Extract product prices from Walmart's website
Monitor price changes and identify deals
Save and analyze deal data
Extend functionality with email alerts

This system will help you stay informed about significant price drops and special offers, allowing you to time your purchases for maximum savings during major sales events.