How To Scrape Google Maps: Detailed Guide 2025

In the digital age, scraping Google Maps can provide invaluable insights for applications such as market research, competitor analysis, and business intelligence. This guide will walk you through the process of using Python to scrape Google Maps, with a particular focus on leveraging the robust capabilities of the Oxylabs Google Maps Scraper API.

Introduction

Scraping Google Maps allows developers and businesses to gather detailed data about businesses, including names, addresses, phone numbers, ratings, and more. This data can be used for various purposes, such as analyzing market trends, enhancing business directories, and conducting location-based research.

Understanding Google Maps Scraping

Google Maps is a powerful tool that provides detailed information about businesses and locations. By scraping this data, you can collect comprehensive datasets that are beneficial for both businesses and developers. The scraped data can be used to analyze market trends, perform competitive analysis, and improve business strategies.

Prerequisites and Tools

To get started with scraping Google Maps, you will need the following tools:

• Python: A versatile programming language widely used for web scraping.
• Requests: A library for making HTTP requests.
• BeautifulSoup: A library for parsing HTML and XML documents.
• Pandas: A library for data manipulation and analysis.
• Oxylabs Google Maps Scraper API: A powerful API designed to handle large-scale data scraping from Google Maps efficiently.

Install the Required Libraries

First, ensure you have Python installed. You can download it from the official Python website. Next, install the necessary libraries:

pip install requests beautifulsoup4 pandas

Setting Up the Scraper

Step 1: Setting up the Environment

To start, set up your Python environment by installing the required packages:

pip install requests beautifulsoup4 pandas

Step 2: Import the Required Libraries

Create a new Python file and import the required libraries:

import requests
import pandas as pd
from bs4 import BeautifulSoup

Step 3: Structure the Payload

The Oxylabs Google Maps Scraper API requires a specific payload structure. Here’s an example of how to structure the payload:

payload = {
    "source": "google_maps",
    "query": "restaurants in New York",
    "domain": "com",
    "geo_location": "New York,United States",
    "user_agent_type": "desktop",
    "parse": True
}

Step 4: Make the Request

Use the following code to send a POST request to Oxylabs’ API:

response = requests.request(
    "POST",
    "https://realtime.oxylabs.io/v1/queries",
    auth=('USERNAME', 'PASSWORD'),
    json=payload
)

if response.status_code == 200:
    print("Request successful!")
else:
    print("Request failed:", response.status_code)

Replace 'USERNAME' and 'PASSWORD' with your Oxylabs API credentials.

Step 5: Extract and Save the Data

Extract the required data from the response and save it in a CSV file:

result = response.json()["results"][0]["content"]
businesses = result["results"]["organic"]

# Create a DataFrame
df = pd.DataFrame(columns=["Name", "Address", "Phone", "Rating", "Reviews"])for business in businesses:
    name = business.get("name")
    address = business.get("address")
    phone = business.get("phone")
    rating = business.get("rating")
    reviews = business.get("reviews")    df = df.append({"Name": name, "Address": address, "Phone": phone, "Rating": rating, "Reviews": reviews}, ignore_index=True)# Save the data to CSV
df.to_csv("google_maps_data.csv", index=False)

Handling CAPTCHAs and Using Proxies

Google Maps may implement CAPTCHAs to prevent automated access. Using proxies can help distribute your requests and reduce the likelihood of encountering CAPTCHAs. The Oxylabs Google Maps Scraper API is designed to handle such challenges, providing a seamless scraping experience.

Conclusion

Scraping Google Maps using Python can unlock a wealth of data for various applications. By following the steps outlined in this guide and utilizing the Oxylabs Google Maps Scraper API, you can efficiently gather and analyze data from Google Maps. Remember to scrape ethically and comply with Google’s terms of service.

Frequently Asked Questions

Is it legal to scrape Google Maps?

When scraping Google Maps, it’s important to consider the website’s terms of service, copyright laws, and ethical guidelines. Google’s Terms of Service prohibit automatic access without consent, and scraping job listings may lead to legal issues. Always review the terms of service and seek legal advice if necessary.

What tools can I use for scraping Google Maps?

In addition to Python and the Requests library, using a robust API like the Oxylabs Google Maps Scraper can streamline the process and handle large-scale scraping efficiently. Other tools include BeautifulSoup for HTML parsing and Selenium for browser automation.