How to Scrape Google Maps Data: Complete Guide for 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

To scrape Google Maps with Python, send a request to AlterLab's API specifying the target URL and desired output format. The service returns fully rendered HTML or structured JSON, which you can parse with standard libraries like BeautifulSoup or json. Adjust parameters such as min_tier and formats to match the complexity of the page and the data you need.

Why collect local data from Google Maps?

Local listings on Google Maps contain valuable signals for businesses and researchers. Common use cases include:

• Monitoring competitor store hours, ratings, and price ranges for market analysis.
• Aggregating points of interest (POIs) such as restaurants, hotels, or retail outlets for geographic datasets.
• Tracking changes in business opening status or service areas over time to detect economic trends.

These datasets are public, frequently updated, and easy to ingest once the rendering barrier is overcome.

Technical challenges

Google Maps pages are built with heavy client‑side frameworks that require a headless browser to execute JavaScript and generate the visible content. The site also implements:

• Rate limiting per IP address.
• Bot detection mechanisms that challenge non‑browser traffic with CAPTCHAs or JavaScript puzzles.
• Dynamic loading of markers and details as the user pans or zooms.

Plain HTTP requests return a minimal shell; the rich list of places and their attributes appear only after client‑side scripts run. AlterLab's Smart Rendering API provisions a managed Chromium instances, rotates residential proxies, and automatically solves challenges, delivering the final DOM ready for parsing.

Quick start with AlterLab API

First, install the official Python SDK and authenticate with your API key. See the Getting started guide for detailed setup.

import alterlab
from bs4 import BeautifulSoup

client = alterlab.Client("YOUR_API_KEY")

# Request a rendered page; specify output as HTML for parsing
response = client.scrape(
    url="https://www.google.com/maps/search/coffee+shops+near+San+Francisco,CA",
    params={"formats": ["html"], "min_tier": 4}
)

soup = BeautifulSoup(response.text, "html.parser")
print(soup.prettify()[:2000])  # inspect first 2000 chars

Equivalent cURL call:

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.google.com/maps/search/coffee+shops+near+San+Francisco,CA",
    "params": {"formats": ["html"], "min_tier": 4}
  }'

The min_tier parameter ensures the request uses a rendering tier capable of executing the map's JavaScript (tier 4 or higher typically suffices). The API returns the fully rendered markup, which you can then query with CSS selectors.

Extracting structured data

Once you have the rendered HTML, target the elements that contain the place information. On Google Maps, each result appears within a container bearing a class like Nv2PK or bfdHYe. Inside, you will find:

• The place name in an element with role="heading".
• The rating inside a span with aria-label containing “out of 5 stars”.
• The address within a span bearing the class Io6YTe.
• The number of reviews in a span with aria-label ending in “reviews”.

Example extraction loop:

places = []
for card in soup.select(".Nv2PK"):
    name_el = card.select_one('[role="heading"]')
    rating_el = card.select_one('span[aria-label*="out of 5 stars"]')
    address_el = card.select_one('.Io6YTe')
    reviews_el = card.select_one('span[aria-label*="reviews"]')

    places.append({
        "name": name_el.get_text(strip=True) if name_el else None,
        "rating": float(rating_el.get_text(strip=True).split()[0]) if rating_el else None,
        "address": address_el.get_text(strip=True) if address_el else None,
        "reviews": int(reviews_el.get_text(strip()).split()[0]) if reviews_el else None,
    })

print(f"Extracted {len(places)} places")

If you prefer JSON output, AlterLab can return structured data directly via the Cortex AI extractor. Set formats: ["json"] and provide a simple schema prompt; the service will attempt to locate the fields automatically.

Best practices

• Rate limiting: Issue no more than one request per second per IP unless you have purchased higher concurrency. Use time.sleep or a token‑bucket limiter.
• Robots.txt: Review https://www.google.com/robots.txt for any disallowed paths related to maps; although the search results page is generally accessible, respect any crawl‑delay directives.
• Handling dynamic content: If you need to trigger actions like clicking “More results”, use the wait_for parameter to pause for a selector to appear before returning the HTML.
• Data freshness: For monitoring, schedule re‑scrapes at intervals that match the expected update frequency (e.g., hourly for price‑sensitive data, daily for static listings).
• Error handling: Check response status codes; AlterLab returns 429 if you exceed your plan’s rate limit and 502 if rendering fails. Implement exponential backoff.

Scaling up

When you need to scrape hundreds or thousands of queries, batch them using the API’s endpoint that accepts an array of URLs. Combine with a job queue (e.g., Celery or RQ) to manage retries and concurrency. For recurring tasks, AlterLab’s scheduling feature lets you define cron expressions; see the Scheduling section in the docs.

Cost scales linearly with the number of successful requests and the rendering tier used. Refer to the AlterLab pricing page for per‑request rates and volume discounts. To keep expenses predictable, set a monthly balance alert and enable automatic throttling when you approach your limit.

Key takeaways

• Google Maps requires JavaScript rendering; plain HTTP requests are insufficient.
• AlterLab’s API abstracts headless browsing, proxy rotation, and challenge solving.
• Extract public data with CSS selectors or request JSON output via Cortex AI.
• Apply rate limiting, review robots.txt, and handle errors responsibly.
• Scale with batch requests, scheduling, and monitoring of usage and spend.

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