How to Scrape Google Maps Data: Complete Guide (2026)

Why Scrape Google Maps Data?

Google Maps is the largest directory of local businesses on the planet — over 200 million listings with verified contact information. When you scrape Google Maps data, you unlock a goldmine of business names, phone numbers, websites, email addresses, ratings, and reviews that would take weeks to collect manually.

Lead generation agencies, SaaS companies, market researchers, and real estate investors all scrape Google Maps data to build targeted prospect lists. The alternative — buying lead lists from ZoomInfo or Apollo at $100-1,500/month — gives you stale data with poor coverage of local businesses. When you scrape Google Maps data yourself, every lead is fresh, verified by Google, and includes fields that paid databases simply don't have.

What Data Can You Scrape from Google Maps?

When you scrape Google Maps data, you can extract 15-25 fields per business listing. Here's what a comprehensive scraper pulls:

• Business name — the verified listing name on Google Maps
• Address — full street address with city, state, and ZIP
• Phone number — primary contact number from the listing
• Website URL — the business's website linked in their Google profile
• Email address — crawled from the business website (not on Google Maps itself)
• Google rating — average star rating (1.0-5.0)
• Review count — total number of Google reviews
• Business hours — open/close times for each day
• Category — primary and secondary business categories
• Google Maps URL — direct link to the business listing
• Coordinates — latitude and longitude for geolocation

The email field is the most valuable when you scrape Google Maps data for lead generation. Google Maps doesn't display emails directly — the best scraping tools crawl each business's website to find contact emails. Our 2026 data report found that 68% of scraped businesses have a discoverable email address.

Who Scrapes Google Maps Data (and Why)?

• Lead generation agencies: scrape Google Maps data to build prospect lists for clients across industries — from dentists to HVAC contractors
• SaaS sales teams: scrape Google Maps data to find local businesses that need their software (POS, scheduling, marketing tools)
• Market researchers: scrape Google Maps data to analyze business density, ratings, and competitive landscapes by geography
• Real estate investors: scrape Google Maps data to identify commercial tenants, assess foot traffic potential, and find property owners
• Cold email marketers: scrape Google Maps data to power personalized outreach campaigns with fresh, verified contact information

5 Best Methods to Scrape Google Maps Data

There are five proven ways to scrape Google Maps data in 2026. Each method has different tradeoffs for speed, cost, technical skill, and data quality. Here's how they compare — and which one fits your use case.

Method 1 — Online Scraping Tools (Fastest)

The fastest way to scrape Google Maps data is with an online tool like GMapsScraper.io. Type your keyword and location, click search, and get 100-500 business records in 30 seconds — with emails included. No installation, no code, no proxies.

Online tools are ideal when you need to scrape Google Maps data without any technical setup. The free tier at GMapsScraper.io gives you 100 leads per search with full email extraction. For agencies and sales teams that scrape Google Maps data regularly, paid plans at $29/month unlock unlimited searches.

Method 2 — Python Scripts (Most Flexible)

Python is the most popular language for developers who scrape Google Maps data programmatically. You can use libraries like Selenium or Playwright for direct browser automation, or call a Google Maps scraper API for a simpler approach. Python gives you full control over the scraping logic, data processing, and export format.

The downside: DIY Python scripts to scrape Google Maps data require ongoing maintenance. Google frequently updates their DOM structure, breaking Selenium-based scrapers every few weeks. Using an API eliminates this maintenance burden while keeping the flexibility of Python for data processing.

Method 3 — Chrome Extensions

Chrome extensions like Instant Data Scraper and Data Miner let you scrape Google Maps data directly from your browser. They're easy to install and work by detecting table-like structures on Google Maps pages. However, most extensions to scrape Google Maps data have significant limitations — they typically cap results at 20-100 per session and don't extract emails.

We covered this in detail in our extension vs online tools comparison. Extensions are fine for quick one-off scrapes, but if you regularly scrape Google Maps data at volume, online tools or APIs are more reliable and faster.

Method 4 — APIs (Best for Automation)

If you need to scrape Google Maps data on a schedule — daily, weekly, or triggered by events — an API is the best approach. APIs let you programmatically scrape Google Maps data and pipe results directly into your CRM, email tool, or database without any manual steps.

GMapsScraper.io's REST API makes it trivial to scrape Google Maps data from any programming language. Send a POST request with your keyword and location, get structured JSON back in 30 seconds. Check our API tutorial for complete code examples in Python and Node.js.

Method 5 — No-Code Tools (n8n, Make.com)

No-code automation platforms like n8n and Make.com let you scrape Google Maps data as part of larger workflows — without writing any code. You can set up a workflow that scrapes Google Maps data, filters by rating, enriches with email, and sends results to Google Sheets or HubSpot automatically.

The tradeoff: no-code tools add a layer of abstraction that makes debugging harder, and they rely on third-party connectors (like Apify actors) that can break. For simple automations, they work well. For serious volume when you scrape Google Maps data at scale, a direct API integration is more reliable.

How to Scrape Google Maps Data with Python

Python is the go-to language for developers who scrape Google Maps data. This section gives you production-ready code that works today — using an API for reliability instead of brittle browser automation.

Prerequisites and Setup

To scrape Google Maps data with Python, you need just two packages:

pip install requests pandas

That's it — no Selenium, no ChromeDriver, no proxy configuration. When you scrape Google Maps data through an API, all the infrastructure complexity is handled server-side. Sign up at GMapsScraper.io (free) and grab your API key.

Complete Python Script (Copy-Paste Ready)

Here's a complete Python script to scrape Google Maps data across multiple cities and export to CSV:

import requests
import pandas as pd
import time

API_KEY = "your_api_key_here"
BASE_URL = "https://gmapsscraper.io/api/v1/search"

def scrape_maps(keyword, location, limit=200):
    """Scrape Google Maps data for a keyword + location."""
    resp = requests.post(BASE_URL,
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "keyword": keyword,
            "location": location,
            "limit": limit
        })
    resp.raise_for_status()
    return resp.json()["results"]

# Define your search targets
searches = [
    ("dentists", "Chicago, IL"),
    ("dentists", "Houston, TX"),
    ("dentists", "Phoenix, AZ"),
    ("plumbers", "Chicago, IL"),
    ("plumbers", "Houston, TX"),
]

# Scrape Google Maps data for all targets
all_leads = []
for keyword, city in searches:
    print(f"Scraping {keyword} in {city}...")
    results = scrape_maps(keyword, city)
    all_leads.extend(results)
    time.sleep(2)  # Respect rate limits

# Deduplicate by phone number
df = pd.DataFrame(all_leads)
df = df.drop_duplicates(subset=["phone"], keep="first")

# Export to CSV
df.to_csv("google_maps_leads.csv", index=False)
print(f"Done! {len(df)} unique leads saved.")

This script will scrape Google Maps data for 5 keyword-city combinations, deduplicate results by phone number, and export everything to a clean CSV. Total runtime: under 2 minutes for 1,000+ leads.

Scraping Google Maps Reviews with Python

Beyond basic business data, you can also scrape Google Maps data that includes individual reviews — useful for sentiment analysis, competitor research, and reputation monitoring:

# Scrape Google Maps review data for a specific place
def scrape_reviews(place_id, limit=50):
    resp = requests.post(
        f"{BASE_URL}/reviews",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"place_id": place_id, "limit": limit}
    )
    return resp.json()["reviews"]

# Get reviews for top-rated businesses
for _, row in df.nlargest(10, "rating").iterrows():
    reviews = scrape_reviews(row["place_id"])
    print(f"{row['name']}: {len(reviews)} reviews scraped")

Handling Anti-Bot Detection

If you scrape Google Maps data directly with Selenium or Playwright, you'll hit CAPTCHAs and IP blocks after 50-200 requests. Here's how to avoid detection:

• Use an API instead: the simplest solution — let the API provider handle proxy rotation and anti-detection. When you scrape Google Maps data through GMapsScraper.io, you never see a CAPTCHA.
• Rotate residential proxies: if you must scrape directly, use residential proxies ($20-100/month) that rotate IPs per request.
• Add random delays: wait 3-10 seconds between requests (randomized) to mimic human behavior.
• Use stealth plugins: playwright-stealth or undetected-chromedriver to bypass common fingerprinting checks.

For most users who scrape Google Maps data, using a managed API eliminates anti-bot issues entirely. You focus on using the data, not fighting infrastructure problems.

How to Scrape Google Maps Data for Free

You don't need a budget to start scraping Google Maps data. Here are the best free options — ranked by data quality and ease of use.

Free Online Tools

GMapsScraper.io offers the most generous free tier to scrape Google Maps data — 100 leads per search with full email extraction, no credit card required. You can run unlimited searches, making the free tier viable for small-scale lead generation and testing before upgrading.

Other free options to scrape Google Maps data online: Outscraper gives you 25 free requests/month (basic data, no emails), and SerpApi offers 100 free searches/month (no emails). For most users, GMapsScraper.io's free tier provides the best value because it's the only tool that includes email extraction at no cost.

Free Python Libraries

Open-source libraries let you scrape Google Maps data for free — if you're willing to handle the infrastructure:

• Selenium + BeautifulSoup: automate Chrome to load Google Maps, parse HTML with BeautifulSoup. Free, but requires proxies ($20-100/month) to avoid blocks.
• Playwright: modern browser automation that's faster than Selenium. Same proxy requirements when you scrape Google Maps data at volume.
• googlemaps-scraper (GitHub): open-source Python package specifically for Google Maps. Works for small batches, but breaks frequently when Google updates their frontend.

Free Chrome Extensions

Several Chrome extensions let you scrape Google Maps data for free:

• Instant Data Scraper: auto-detects table data on any page. Free, but limited to visible results (20-40 per page).
• Data Miner: 500 free scrapes/month. More structured than Instant Data Scraper, but still no email extraction.
• Google Maps Extractor: purpose-built for Maps, but the free version caps at 10 results per search.

Free vs Paid — Honest Comparison

Here's the truth about free methods to scrape Google Maps data: they work for testing and small-scale extraction, but hit walls quickly at volume. Free Chrome extensions cap at 20-100 results. Free Python scripts need proxies (which aren't free). Open-source scrapers break every 2-4 weeks.

The exception is GMapsScraper.io's free tier — 100 leads per search with emails, unlimited searches. For teams that scrape Google Maps data regularly, the $29/month paid plan pays for itself in the first hour of saved manual work. Use our lead value calculator to see the ROI for your specific industry.

Scrape Google Maps Data at Scale (1,000+ Businesses)

Scraping 100 businesses is easy. Scraping 10,000 across 50 cities without duplicates, broken data, or IP blocks — that's where most people fail. Here's the proven strategy to scrape Google Maps data at scale.

Multi-City Extraction Strategy

The most effective way to scrape Google Maps data at scale is to pick one high-value niche and expand horizontally across cities. For example, if dentists convert well for your agency, scrape dentists in the top 50 US metro areas. Use our Bulk Keywords Generator to create search queries for every city in seconds.

# Multi-city strategy to scrape Google Maps data
cities = [
    "New York, NY", "Los Angeles, CA", "Chicago, IL",
    "Houston, TX", "Phoenix, AZ", "Philadelphia, PA",
    "San Antonio, TX", "San Diego, CA", "Dallas, TX",
    "San Jose, CA", "Austin, TX", "Jacksonville, FL",
    "Fort Worth, TX", "Columbus, OH", "Charlotte, NC",
    "Indianapolis, IN", "San Francisco, CA", "Seattle, WA",
    "Denver, CO", "Nashville, TN",
]

keyword = "dentists"
all_results = []

for city in cities:
    results = scrape_maps(keyword, city)
    all_results.extend(results)
    print(f"{city}: {len(results)} leads")
    time.sleep(2)

print(f"Total: {len(all_results)} leads across {len(cities)} cities")

Data Deduplication

When you scrape Google Maps data across multiple cities, chain businesses (franchises, multi-location practices) appear in every city. Without deduplication, you'll send the same business 3-5 duplicate outreach emails — which kills your sender reputation and wastes time.

# Deduplicate scraped Google Maps data
df = pd.DataFrame(all_results)

# Primary dedup: phone number (most reliable)
df = df.drop_duplicates(subset=["phone"], keep="first")

# Secondary dedup: business name + city
df = df.drop_duplicates(
    subset=["name", "address"],
    keep="first"
)

# Filter out closed businesses
df = df[df["status"] != "CLOSED_PERMANENTLY"]

print(f"After dedup: {len(df)} unique businesses")

Export Formats (CSV, JSON, Excel)

After you scrape Google Maps data, you need it in the right format for your workflow. Most tools support direct export to Excel or CSV. For CRM integration, JSON is usually the best choice:

• CSV: best for importing into email tools (Mailshake, Instantly, Lemlist)
• Excel (.xlsx): best for sharing with clients or team members who prefer spreadsheets
• JSON: best for API-to-API integrations (CRM, database, webhook pipelines)

What Data Fields Can You Scrape from Google Maps?

Not every tool lets you scrape Google Maps data with the same depth. The data fields you get depend on your scraping method — basic scrapers return 8-10 fields, while comprehensive tools extract 20+ fields per business.

Standard Fields (Every Scraper Gets These)

Regardless of which tool you use to scrape Google Maps data, you can expect these core fields from every Google Maps listing:

Advanced Fields (Email, Social, Owner Info)

Premium tools that scrape Google Maps data go beyond what's visible on the listing page. They crawl each business's website to extract contact details that Google doesn't display:

• Email address: crawled from the business website (contact page, footer, about page). The most valuable field when you scrape Google Maps data for outreach.
• Social media links: Facebook, Instagram, LinkedIn, Twitter URLs found on the website
• Owner/manager name: extracted from the "About" or team page
• Business hours (detailed): open/close time for each day of the week
• Price range: $, $$, $$$, or $$$$ pricing indicator
• Place ID: Google's unique identifier, useful for API integrations
• Photos count: number of photos on the listing (indicates engagement level)

GMapsScraper.io extracts all of these fields — including email — in a single request. When you scrape Google Maps data with tools that skip email crawling, you lose the most valuable data point for cold outreach. Our email scraper guide covers email extraction methods in depth.

Data Quality by Industry

When you scrape Google Maps data, data quality varies significantly by industry. Some industries have nearly 100% phone coverage but low email availability, while others are the opposite. Here are the key findings from our research:

• Restaurants: 95% phone, 80% website, but only 35% email — many use order platforms instead of email
• Dentists: 98% phone, 90% website, 72% email — highly digitized, great for outreach
• Lawyers: 97% phone, 88% website, 78% email — professional services have the best email coverage
• Plumbers: 90% phone, 55% website, 30% email — many smaller operators don't have websites
• Real Estate: 85% phone, 75% website, 65% email — individual agents vs. firms varies widely

Is It Legal to Scrape Google Maps Data?

This is the most-asked question: is it legal to scrape Google Maps data? The short answer is yes — scraping publicly available business information is legal in the US and EU. But there are nuances you should understand before you scrape Google Maps data at scale.

Court Rulings on Web Scraping

The landmark case is hiQ Labs v. LinkedIn (2022), where the US Ninth Circuit ruled that scraping publicly available data does not violate the Computer Fraud and Abuse Act (CFAA). This ruling established a clear precedent: if data is publicly visible to any website visitor, automated collection is legal.

When you scrape Google Maps data, you're collecting the same business information that any Google Maps user can see — names, addresses, phone numbers, ratings, and reviews. This is public business data, not personal data, and courts have consistently protected the right to collect it programmatically.

Google's Terms of Service

Google's Terms of Service technically prohibit automated access to their services. However, ToS violations are civil matters (breach of contract), not criminal. No individual or company has been successfully sued by Google for scraping public business data from Google Maps. Using a managed tool to scrape Google Maps data adds a layer of separation — you're calling a third-party API, not directly scraping Google.

Best Practices for Compliant Scraping

To stay on the right side when you scrape Google Maps data:

• Only collect public business data — don't scrape personal profiles or private information
• Follow CAN-SPAM for outreach — include an opt-out mechanism in every cold email
• Comply with GDPR — if targeting EU businesses, process data lawfully under legitimate interest
• Don't overload servers — use reasonable rate limits (2-5 second delays between requests)
• Use data for legitimate purposes — B2B outreach, market research, and competitive analysis are all legitimate
• Use a managed API — providers like GMapsScraper.io handle responsible data collection practices on their infrastructure

Common Mistakes When You Scrape Google Maps Data

After helping thousands of users scrape Google Maps data, we've seen the same mistakes cost people time, money, and email sender reputation. Here's what to avoid — and how to fix each issue.

Scraping Without Proxies

If you scrape Google Maps data directly from your IP address using Selenium or Playwright, you'll get blocked after 50-200 requests. Google detects automated access patterns and serves CAPTCHAs or blank results. The fix: use a managed API that handles proxy rotation automatically, or invest in residential proxies ($20-100/month) if you insist on direct scraping.

Ignoring Rate Limits

Blasting requests as fast as possible when you scrape Google Maps data is counterproductive. Even with proxies, you need 2-5 second delays between requests. With API-based scraping, respect the provider's rate limits and implement exponential backoff for 429 responses. Slow and steady scraping gives you 100% data completion; rushing gives you blocks and partial data.

Not Validating Email Data

When you scrape Google Maps data that includes emails, not all addresses are valid. Catch-all domains accept any email address (making validation impossible), some businesses use form-only contact pages, and website changes may have outdated contact info. Always validate emails before sending outreach — a 30%+ bounce rate will destroy your sender domain reputation.

Scraping Closed Businesses

Google Maps keeps permanently closed businesses in its index. If you scrape Google Maps data without filtering by status, you'll include businesses that no longer exist — wasting outreach effort and making your agency look unprofessional. Always filter status != "CLOSED_PERMANENTLY" before exporting your data.

Frequently Asked Questions

Can you scrape Google Maps data for free?

Yes. GMapsScraper.io lets you scrape Google Maps data for free — 100 leads per search with email extraction included. No credit card required. Open-source Python scripts on GitHub also work for free, but require proxy setup and ongoing maintenance. Chrome extensions like Instant Data Scraper offer free scraping but are limited to 20-40 results per session.

Is it legal to scrape Google Maps data?

Yes. Scraping publicly available business data is legal in the US and EU under the hiQ Labs v. LinkedIn precedent (2022). When you scrape Google Maps data, you're collecting public business information — names, addresses, phone numbers, and ratings — that any Google Maps user can see. For email outreach, follow CAN-SPAM and GDPR requirements.

What is the best tool to scrape Google Maps data?

For most users, GMapsScraper.io is the best tool to scrape Google Maps data — it's the only tool offering free email extraction, 100 leads per search on the free tier, and a simple API for automation. For developers who prefer Python, the GMapsScraper.io API combined with the requests library provides the best balance of simplicity and flexibility.

How to scrape Google Maps data with Python?

Install requests and pandas, get a free API key from GMapsScraper.io, and send a POST request with your keyword and location. The API returns structured JSON that you can convert to a DataFrame and export to CSV. See the complete Python script in the section above — it takes 5 minutes to set up and scrapes Google Maps data for multiple cities with deduplication built in.

How many businesses can you scrape from Google Maps?

Google Maps returns up to 500 results per search query. To scrape Google Maps data for more businesses, use multiple search queries — different keywords, different cities, or more specific locations (neighborhoods instead of cities). With GMapsScraper.io's paid plan ($29/month), you can scrape 2,000-10,000 leads per day across unlimited searches.

How to scrape Google Maps data without getting blocked?

The simplest way to scrape Google Maps data without blocks is to use a managed API like GMapsScraper.io — it handles proxy rotation, rate limiting, and anti-detection automatically. If you scrape directly with Python, use residential proxies, add 3-10 second random delays between requests, and use stealth browser libraries like playwright-stealth.