Building a Walmart Search Data Scraping System with Python (Code Included)

2025-03-14

Introduction

In the e-commerce industry, obtaining accurate and timely search data is crucial for market analysis, competitive research, and product selection. Walmart, as one of the largest retailers globally, holds a vast amount of product data that businesses and developers can leverage by scraping its search results to gather valuable insights. In this article, we will demonstrate how to use Python in combination with the Luckdata API to automate the scraping of Walmart search data and perform data storage and analysis.

1. Why Scrape Walmart Search Data?

1.1 Business Value

Market Trend Analysis: By collecting search data, you can analyze consumer demand changes for different products.
Competitive Research: Monitor competitors' product rankings, price changes, and sales.
Inventory & Pricing Adjustments: E-commerce sellers can adjust pricing strategies and inventory management based on market conditions.

1.2 Technical Value

Automated Data Scraping: Use APIs or scraping technologies to regularly obtain data, reducing manual effort.
Structured Data Handling: Store collected data in databases for easy analysis and future machine learning applications.

2. Using Luckdata API to Scrape Walmart Search Data

Luckdata offers a specialized Walmart API that allows developers to efficiently retrieve search results. We can use its API to obtain product data related to specific keywords, including titles, prices, ratings, and reviews.

2.1 API Overview

Luckdata's Walmart API supports searching products by keyword. The API request format is as follows:

GET /api/walmart-api/{API_KEY}?url=https://www.walmart.com/search?q={keyword}

Where:

{API_KEY} is your Luckdata API key
{keyword} is the search keyword, such as "samsung galaxy"

3. Scraping Data Using Python

First, ensure that you have the requests library installed in your development environment:

pip install requests

3.1 Python Code Example

import requests
import json
# Set your API Key
API_KEY = "your_key"
# Define the search keyword
keyword = "samsung galaxy"
url = f"https://luckdata.io/api/walmart-api/{API_KEY}?url=https://www.walmart.com/search?q={keyword}"
# Set request headers
headers = {
"X-Luckdata-Api-Key": API_KEY
}
# Make the request
response = requests.get(url, headers=headers)
# Parse the JSON response
if response.status_code == 200:
data = response.json()
print(json.dumps(data, indent=4, ensure_ascii=False))
else:
print(f"Request failed with status code: {response.status_code}")

4. Storing Data Locally or in a Database

Once we obtain the data, we can store it in a CSV file or a database for later analysis.

4.1 Storing Data in CSV

import csv
# Assuming the data retrieved from the API
products = [
{"title": "Samsung Galaxy S23", "price": "$799", "rating": "4.5", "reviews": "1200"},
{"title": "Samsung Galaxy S22", "price": "$699", "rating": "4.4", "reviews": "950"},
]
# Save data to CSV
with open("walmart_search_results.csv", "w", newline="", encoding="utf-8") as file:
writer = csv.DictWriter(file, fieldnames=["title", "price", "rating", "reviews"])
writer.writeheader()
writer.writerows(products)
print("Data has been successfully saved to a CSV file")

4.2 Storing Data in MySQL

import mysql.connector
# Connect to MySQL database
conn = mysql.connector.connect(
host="localhost",
user="root",
password="your_password",
database="walmart_data"
)
cursor = conn.cursor()
# Create table if not exists
cursor.execute("""
CREATE TABLE IF NOT EXISTS walmart_products (
id INT AUTO_INCREMENT PRIMARY KEY,
title VARCHAR(255),
price VARCHAR(50),
rating VARCHAR(10),
reviews VARCHAR(10)
)
""")
# Insert data into the table
for product in products:
cursor.execute("""
INSERT INTO walmart_products (title, price, rating, reviews)
VALUES (%s, %s, %s, %s)
""", (product["title"], product["price"], product["rating"], product["reviews"]))
# Commit the transaction
conn.commit()
cursor.close()
conn.close()
print("Data has been saved to MySQL")

5. Analyzing Data: Extracting Market Trends

We can perform basic analysis on the collected data, such as:

5.1 Finding the Most Popular Products

import pandas as pd
# Load the CSV data
df = pd.read_csv("walmart_search_results.csv")
# Sort by rating
top_products = df.sort_values(by="rating", ascending=False)
# Display top 5 products
print(top_products.head())

5.2 Visualizing Data

import matplotlib.pyplot as plt
# Convert rating column to numeric
df["rating"] = df["rating"].astype(float)
# Plot the distribution of ratings
plt.hist(df["rating"], bins=10, color="blue", alpha=0.7)
plt.xlabel("Rating")
plt.ylabel("Product Count")
plt.title("Walmart Search Product Rating Distribution")
plt.show()

6. Automation & Expansion

To make the system more practical, we can expand it with the following approaches:

Automate Regular Data Fetching: Use cron (Linux) or Task Scheduler (Windows) to run the scraping script at scheduled intervals.
Enhance Data Cleaning: Filter out invalid data and handle missing values.
Integrate AI for Sales Predictions: Combine the scraped data with machine learning models to predict future sales trends and provide actionable business insights.

Conclusion

In this article, we learned how to use Python and the Luckdata API to automate the process of retrieving Walmart search data and perform data storage and analysis. Our system can assist e-commerce businesses, data analysts, and developers in quickly gathering market insights, thus enabling more informed business strategies. Moving forward, this data can be combined with AI and big data technologies to further enhance predictive capabilities. If you're interested in data scraping or analysis, you can try optimizing this system to make it more efficient and intelligent!