import json
import requests
from bs4 import BeautifulSoup
from urllib.parse import urlencode

def create_walmart_product_url(product):
    return 'https://www.walmart.com' + product.get('canonicalUrl', '').split('?')[0]

headers={"User-Agent": "Mozilla/5.0 (iPad; CPU OS 12_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148"}
product_url_list = []
keyword = 'ipad'

for page in range(1, 26):
    try:
        payload = {'q': keyword, 'sort': 'best_seller', 'page': page, 'affinityOverride': 'default'}
        walmart_search_url = 'https://www.walmart.com/search?' + urlencode(payload)
        response = requests.get(walmart_search_url, headers=headers)
        if response.status_code == 200:
            html_response = response.text
            soup = BeautifulSoup(html_response, "html.parser")
            script_tag = soup.find("script", {"id": "__NEXT_DATA__"})
            if script_tag is not None:
                json_blob = json.loads(script_tag.get_text())
                product_list = json_blob["props"]["pageProps"]["initialData"]["searchResult"]["itemStacks"][0]["items"]
                product_urls = [create_walmart_product_url(product) for product in product_list]
                product_url_list.extend(product_urls)
                if len(product_urls) == 0:
                    break
    except Exception as e:
        print('Error', e)

print(product_url_list)

The output will be a list of product URLs.

Step 2: Scraping Walmart Product Data

The Walmart Search request also returns a lot more information than just the product URLs. You can get the product name, price, image URL, rating, and the number of reviews from the JSON blob as well. Depending on what data you need, you might not need to request each product page because you can get the data from the search results.

To extract the product data from the list, you can use a function like this:

def extract_product_data(product):
    return {
        'url': create_walmart_url(product),
        'name':Based on the information I found, here's a Python script that scrapes Walmart.com for product information. The script generates a list of product URLs and then extracts product data from each URL. 

Please note that this script only works for up to 25 pages of search results per query due to Walmart's limitations. If you need to scrape more data, you'll need to modify your queries or change the sorting parameters.

Here's the Python script:

```python
import json
import requests
from bs4 import BeautifulSoup
from urllib.parse import urlencode

def create_walmart_product_url(product):
    return 'https://www.walmart.com' + product.get('canonicalUrl', '').split('?')[0]

def extract_product_data(product):
    return {
        'url': create_walmart_product_url(product),
        'name': product.get('name', ''),
        'description': product.get('description', ''),
        'image_url': product.get('image', ''),
        'average_rating': product['rating'].get('averageRating'),
        'number_reviews': product['rating'].get('numberOfReviews'),
    }

headers={"User-Agent": "Mozilla/5.0 (iPad; CPU OS 12_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148"}
product_url_list = []
product_data_list = []

# Walmart Search Keyword
keyword = 'ipad'

# Loop Through Walmart Pages Until No More Products
for page in range(1, 5):
    try:
        payload = {'q': keyword, 'sort': 'best_seller', 'page': page, 'affinityOverride': 'default'}
        walmart_search_url = 'https://www.walmart.com/search?' + urlencode(payload)
        response = requests.get(walmart_search_url, headers=headers)

        if response.status_code == 200:
            html_response = response.text
            soup = BeautifulSoup(html_response, "html.parser")
            script_tag = soup.find("script", {"id": "__NEXT_DATA__"})
            if script_tag is not None:
                json_blob = json.loads(script_tag.get_text())
                product_list = json_blob["props"]["pageProps"]["initialData"]["searchResult"]["itemStacks"][0]["items"]
                product_urls = [create_walmart_product_url(product) for product in product_list]
                product_url_list.extend(product_urls)
                product_data = [extract_product_data(product) for product in product_list]
                product_data_list.extend(product_data)
                if len(product_urls) == 0:
                    break

    except Exception as e:
        print('Error', e)

print(product_url_list)
print(product_data_list)

This script will output two lists. product_url_list will contain the URLs of each product, and product_data_list will contain dictionaries with product data (name, description, image URL, average rating, and number of reviews) for each product.