How to Scrape Google Maps in 2026: API and Python Guide

The fastest way to scrape Google Maps in 2026 is to call a structured Maps API that returns normalized JSON — business name, address, phone, website, rating, reviews, and coordinates — instead of driving a headless browser yourself. You can still build a DIY scraper in Python, but Maps' infinite scroll, frequent layout changes, and anti-bot defenses make it expensive to maintain. This guide covers all three approaches, what each returns, where each breaks, and the legal basics.

Is it legal to scrape Google Maps?

Scraping public Google Maps data (business names, addresses, ratings, reviews) sits in the same general category as other public-web scraping. In the US, the hiQ Labs v. LinkedIn litigation held that accessing publicly available data does not violate the Computer Fraud and Abuse Act. That said, Google's Terms of Service prohibit automated access, so you can still face account blocks or cease-and-desist letters, and personal data triggers privacy laws such as GDPR and CCPA. Rules of thumb:

• Collect only public business data; avoid personal data and anything behind a login.
• Respect rate limits and don't degrade the service.
• Review Google's terms and your own compliance requirements.

This is not legal advice — when in doubt, talk to a lawyer.

Option 1: DIY in Python (and why it breaks)

A hand-rolled scraper usually means a headless browser (Playwright or Selenium), because Maps renders results with JavaScript and loads more via infinite scroll:

import csv
from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    page = p.chromium.launch().new_page()
    page.goto("https://www.google.com/maps/search/coffee+shops+in+Austin")

    # scroll the results panel until no new cards load (Maps caps ~120 per search)
    panel = page.locator('div[role="feed"]')
    for _ in range(10):
        panel.evaluate("el => el.scrollBy(0, el.scrollHeight)")
        page.wait_for_timeout(1500)

    rows = [
        {"name": a.get_attribute("aria-label"), "url": a.get_attribute("href")}
        for a in page.locator('div[role="feed"] a[href*="/maps/place/"]').all()
    ]

    with open("places.csv", "w", newline="") as f:
        w = csv.DictWriter(f, fieldnames=["name", "url"])
        w.writeheader(); w.writerows(rows)
# ...then add proxies, CAPTCHA handling, geographic tiling, and a detail request per place.

It works in a demo and then fights you in production:

• Infinite scroll — you have to script the results panel scrolling until no new cards load.
• The ~120-result cap — Maps returns at most ~100–200 places per search viewport, so deeper coverage means splitting the area into geographic tiles and deduping by place ID.
• Layout churn — Google changes the DOM and your selectors silently break.
• Anti-bot — CAPTCHAs and IP bans push you into proxy rotation and fingerprinting.
• Scale — one browser per query is slow, so you end up running and monitoring a browser cluster.

Most of the cost is not the first scrape — it is keeping it alive.

Option 2: Ready-made scraper tools

No-code extractors and marketplace actors handle the browser for you and export CSV or JSON. They are great for one-off lists, but less convenient when you need Maps data inside a product or pipeline, on a schedule, with predictable fields.

Option 3: A structured Google Maps API

For repeatable, in-product workflows, a Google Maps scraping API gives you a documented endpoint that returns normalized JSON — no browser, no selectors, no proxy pool to run. Search by query and location:

curl -X POST https://api.crawlora.net/api/v1/google/map/search \
  -H "x-api-key: $CRAWLORA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "coffee shops in Austin, TX", "limit": 20}'

The same call in Python:

import requests

resp = requests.post(
    "https://api.crawlora.net/api/v1/google/map/search",
    headers={"x-api-key": "YOUR_API_KEY"},
    json={"query": "coffee shops in Austin, TX", "limit": 20},
)
for place in resp.json()["data"]:
    print(place["name"], place.get("rating"), place.get("address"))

A response is normalized JSON you can store directly (fields shown are illustrative — check the docs for the current schema):

{
  "code": 200,
  "msg": "OK",
  "data": [
    {
      "name": "Example Coffee",
      "place_id": "ChIJ...",
      "category": ["coffee_shop"],
      "address": "123 Example St, Austin, TX",
      "phone": "+1 512-555-0100",
      "website": "https://example.com",
      "rating": 4.6,
      "reviews": 318,
      "latitude": 30.2672,
      "longitude": -97.7431
    }
  ]
}

Need the full profile for a specific place? Enrich it with the place details endpoint:

curl https://api.crawlora.net/api/v1/google/map/place/CHIJ_PLACE_ID \
  -H "x-api-key: $CRAWLORA_API_KEY"

What you can collect

Where the public profile exposes them, the fields fall into a few groups:

• Business identity — name, category, place ID, and the Maps URL.
• Contact — address, phone, website, and opening hours.
• Location — latitude and longitude for mapping and radius search.
• Social proof — average rating and review count, plus reviews. The official Places API caps reviews at about five per place unless you own the listing, so sentiment analysis across many businesses needs dedicated review extraction.

Re-run on a schedule and store one row per place per run to track ratings and presence over time.

Limitations and common challenges

A few Google Maps realities to plan around:

• The ~120-result cap. Maps caps results per search viewport, so deep coverage of a city or category means tiling the area into smaller searches and deduping by place ID.
• Personalization. Results vary by location and history; locale parameters reduce but don't eliminate this.
• Reviews are gated on the official API. Place Details returns ~5 reviews per place; full review mining needs a dedicated scraper, and reviewer names/text are potentially personal data.
• Anti-bot for DIY. Direct scraping faces infinite scroll, CAPTCHAs, and fingerprinting; a structured API absorbs scrolling, proxy routing, and retries behind the endpoint.

Where this gets used

Structured Maps data powers a few common workflows:

• Lead generation — build local business lists and enrich a CRM. See the Google Maps lead generation and lead generation API use cases.
• Local market intelligence — map competitor density, ratings, and website presence by area.
• Local SEO research — track categories and ratings across locations.

Sources

Start collecting

Try it first, free: run any public URL through the Free Web Scraper, or check whether a site blocks bots with the Anti-Bot Checker — no signup.

Test the search endpoint in the Playground, check the response schema in the API docs, and review credit costs on the pricing page. Pair local business listings with LinkedIn company data for B2B lead enrichment, or with Google Trends to size category demand by region. Run the same brand and category terms through how to scrape Brave Search to see who ranks in web results, not just on the map. Once you have the listings, how to scrape Google Reviews picks up the review stream attached to each one. To pick a tool, see the best Google Maps scraping APIs in 2026; for the bigger picture, how to choose a web scraping API; and for the rules, is web scraping legal in 2026?.