How to Scrape Data From Google Maps: Your Complete Step-by-Step Guide

Scraping data from Google Maps means using a tool or script to pull business info automatically. This includes names, phone numbers, addresses, ratings, and reviews from Google Maps listings. You don’t have to copy anything by hand. Instead, you save that data into a clean spreadsheet or file for easy use.

If you have ever spent a Monday morning copying business contacts row by row from Google Maps into Excel, you already know the problem. It takes forever. It is tedious. And by the time you finish, the list is already half-outdated. I have seen small business owners spend 2 full hours each week doing exactly that — work that a simple scraper can finish in under 10 minutes.

This guide covers everything you need. You’ll learn what data to pull, the 5 methods that work today, and real Python code to run now. It also highlights the tools worth your time, how to avoid getting blocked, and the legal boundaries. Whether you code daily or have never opened a terminal, there’s a method here for you.

So, Why Does Google Maps Data Matter So Much?

Google Maps holds over 200 million business listings across 220+ countries. Two billion people use it every single month. That is not just a navigation tool — it is the world’s largest public business database.

Every listing has a phone number. Most have a website. Many have reviews, hours, GPS coordinates, and service details. All of it is publicly visible to anyone with a browser. The challenge is not finding the data — it is collecting it at a scale that actually saves you time.

The web scraping market crossed $1.03 billion in 2025 and is growing toward $2 billion by 2030 at a 14.2% annual rate. That growth comes from three places: lead generation, competitive research, and AI training data. Google Maps sits at the center of all three. People are not scraping it because it is trendy — they are doing it because the data is genuinely useful.

What Data Can You Actually Pull From Google Maps?

There are 2 layers here, and the difference between them matters a lot.

Layer 1 is everything you see directly on a Google Maps listing. Layer 2 is what you get when a scraper also visits the business’s linked website and pulls additional contact and marketing details. Most basic tools only give you Layer 1. Dedicated platforms give you both.

Think about what that second column actually gives you. You scrape 500 restaurants in Chicago. Now you do not just know they exist — you know which ones run Facebook ads, which ones have zero email listed anywhere, which ones have not even claimed their Google listing. That is not a contact list. That is a segmented pipeline you can act on immediately.

Who Actually Uses Google Maps Scraping?

This is not just for developers. Real people across many roles use it every week.

Sales teams use it to build prospect lists filtered by city, business type, and star rating. Marketing agencies scrape it to find clients who have websites but no ad presence. Real estate investors use it to map commercial landlords. Researchers use it for market saturation studies. Franchise operators use it to spot expansion gaps.

One workflow I have come across repeatedly: agencies scrape restaurants in mid-size US cities with a rating between 3.5 and 4.2, a website present, but no Facebook ad pixel detected. That is a restaurant with customers but no digital marketing strategy. That is a warm lead. Without scraping, building that list manually would take days. With the right tool, it takes minutes.

The 5 Methods to Scrape Google Maps (Compared Honestly)

There is no single best method for everyone. Each one makes sense depending on your skill level, budget, and how much data you need.

Method 1: No-Code Platforms — The Fastest Way to Start

No-code tools like Scrap.io, Outscraper, and Apify’s Google Maps Scraper handle the entire process for you. You type in a business category and a location, set your filters, and hit export. The result lands in your inbox as a CSV or Excel file.

What makes these tools valuable beyond their simplicity is the pre-export filtering. You can filter by minimum star rating, review count, whether a website exists, whether an email is present, which social platforms they use, even whether they run Facebook ads. You define exactly who you want before you spend a single credit.

Apify’s Google Maps Scraper has collected over 907 reviews with a 4.7 average rating on their platform. Scrap.io processes data in real time, not from a stale cached database. Outscraper has been used to pull over 120,000 data points in a single year for market research projects. These are production tools used by real teams, not hobby projects.

Best for: Sales teams, agencies, non-developers, anyone who needs data fast without building infrastructure.

Method 2: Python Scraping — Full Control, Real Maintenance Cost

Python gives you complete flexibility. You decide exactly what to extract, how to structure it, and how to store it. The main libraries people use are Selenium, Playwright, and BeautifulSoup.

Here is the honest tradeoff: writing the initial script is manageable. Maintaining it is not. Google updates its Maps interface regularly. When that happens, your CSS selectors break. Your scrolling logic stops working. What ran fine on Tuesday fails silently on Wednesday. If you are not actively watching it, you collect bad data or nothing at all.

That said, for developers who want full control or have very specific extraction needs, Python is still the right path. I will walk through working code in the next section.

Best for: Developers with specific requirements, one-time research projects, teams with engineering capacity to maintain scripts.

Method 3: Chrome Extensions — Useful for Small Jobs, Nothing More

Extensions like Instant Data Scraper and Data Miner are free and install in seconds. For a quick one-off scrape of 50 listings, they work. You get the basic visible fields — name, address, rating, hours.

The ceiling is low. Google Maps returns a maximum of 120 results per search. That is a platform cap, not a tool limitation. Extensions also use your own IP address, which means enough requests get you blocked. And none of them pull email addresses or social profiles — you only get what is visible on the page.

Best for: Very small, one-time data grabs where speed matters more than depth.

Method 4: PhantomBuster — Decent Tool, Frustrating Pricing Model

PhantomBuster works. You feed it a Google Maps URL, run their pre-built workflow, and receive a CSV. The problem I have seen people run into repeatedly is the pricing model. They bill by execution time, not by results delivered. Your script runs for 40 minutes and produces 300 clean leads — or it runs for 40 minutes, hits an error at minute 35, and delivers 12 rows of incomplete data. Same price either way.

Best for: Users already inside the PhantomBuster ecosystem who want a quick addition to an existing automation.

Method 5: Google Places API — Official, But Limited

The Places API is Google’s own solution. It is stable, well-documented, and does not risk ToS violations. But it comes with real limitations — only 120 results per query, no email or social data, and pricing that adds up fast at scale ($32–$40 per 1,000 requests depending on the call type).

For small, structured data needs, it is a solid choice. For any kind of serious lead generation or market research at volume, dedicated scraping platforms deliver more data at a lower cost.

Best for: Developers who need official API access, small data pulls, or applications requiring stable and sanctioned data.

How to Scrape Google Maps With Python: Step-by-Step

This section walks through a working Python approach using Selenium. You can follow along exactly, or adapt it to Playwright if you prefer a more modern browser automation library.

Step 1: Set Up Your Environment

Install the required tools first.

pip install selenium webdriver-manager requests pandas

You also need ChromeDriver. The easiest way is to use the webdriver-manager library, which handles version matching automatically.

Step 2: Launch the Browser and Search

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
import time
import pandas as pd

# Launch Chrome (headless mode optional)
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))

# Open Google Maps
driver.get("https://www.google.com/maps")
time.sleep(2)

# Search for your target
search_box = driver.find_element(By.ID, "searchboxinput")
search_box.send_keys("hardware shops in New York")
search_box.send_keys(Keys.ENTER)
time.sleep(3)

Step 3: Scroll to Load All Results

Google Maps loads results dynamically as you scroll. You need to scroll the sidebar panel, not the main window.

# Find the results sidebar
results_panel = driver.find_element(By.CSS_SELECTOR, 'div[role="feed"]')

# Scroll down to load more results
last_height = driver.execute_script("return arguments[0].scrollHeight", results_panel)

while True:
    driver.execute_script("arguments[0].scrollTo(0, arguments[0].scrollHeight);", results_panel)
    time.sleep(2)
    new_height = driver.execute_script("return arguments[0].scrollHeight", results_panel)
    if new_height == last_height:
        break
    last_height = new_height

Step 4: Extract Business Data

businesses = []

results = driver.find_elements(By.CSS_SELECTOR, 'div.Nv2PK')

for result in results:
    try:
        name = result.find_element(By.CSS_SELECTOR, '.qBF1Pd').text
    except:
        name = "N/A"

    try:
        rating = result.find_element(By.CSS_SELECTOR, '.MW4etd').text
    except:
        rating = "N/A"

    try:
        address = result.find_element(By.CSS_SELECTOR, '.W4Efsd span').text
    except:
        address = "N/A"

    businesses.append({
        "Name": name,
        "Rating": rating,
        "Address": address
    })

# Save to CSV
df = pd.DataFrame(businesses)
df.to_csv("google_maps_data.csv", index=False, encoding="utf-8")
print(f"Saved {len(businesses)} results.")

Step 5: Scrape Individual Business Details

To get phone numbers and websites, you need to click each listing and extract from the detail panel.

for result in results:
    result.click()
    time.sleep(2)

    try:
        phone = driver.find_element(By.CSS_SELECTOR, 'button[data-tooltip="Copy phone number"]').text
    except:
        phone = "N/A"

    try:
        website = driver.find_element(By.CSS_SELECTOR, 'a[data-tooltip="Open website"]').get_attribute("href")
    except:
        website = "N/A"

    # Go back to results
    driver.back()
    time.sleep(2)

One important note here — Google changes its HTML structure regularly. The CSS selectors above work as of early 2026, but they may shift. If your script suddenly returns empty results, inspect the page source and update the selectors. This is the number one maintenance burden with DIY Python scrapers.

How to Scrape Google Maps Reviews With Python

Reviews sit inside a dynamic panel that scrolls independently from the rest of the page. This makes them harder to extract than basic listing data.

Load the Review Panel

# Click the "Reviews" tab on a business listing
reviews_tab = driver.find_element(By.XPATH, '//button[contains(@aria-label, "Reviews")]')
reviews_tab.click()
time.sleep(2)

Scroll the Review Panel

scrollable = driver.find_element(By.CSS_SELECTOR, 'div[aria-label="Reviews"]')
last_height = driver.execute_script("return arguments[0].scrollHeight;", scrollable)

while True:
    driver.execute_script("arguments[0].scrollTo(0, arguments[0].scrollHeight);", scrollable)
    time.sleep(2)
    new_height = driver.execute_script("return arguments[0].scrollHeight;", scrollable)
    if new_height == last_height:
        break
    last_height = new_height

Extract Review Content

reviews = driver.find_elements(By.CSS_SELECTOR, '.jftiEf')

for r in reviews:
    try:
        reviewer = r.find_element(By.CSS_SELECTOR, '.d4r55').text
    except:
        reviewer = "N/A"

    try:
        date = r.find_element(By.CSS_SELECTOR, '.rsqaWe').text
    except:
        date = "N/A"

    try:
        text = r.find_element(By.CSS_SELECTOR, '.wiI7pd').text
    except:
        text = "N/A"

    try:
        stars = r.find_element(By.CSS_SELECTOR, 'span[aria-label]').get_attribute('aria-label')
    except:
        stars = "N/A"

    print(reviewer, date, stars, text[:80])

Reviews load slower than listing data. Add pauses between scroll actions. If Google stops loading more reviews mid-session, try reducing scroll speed or adding a random delay between actions.

5 Most Common Mistakes People Make When Scraping Google Maps

I have seen these same mistakes come up repeatedly, and each one either wastes time or breaks the scraper entirely.

Searching too broadly is the most common one. If you search “dentists in California,” Google Maps returns 120 results max. That is not all the dentists in California — that is just the ones Google considers most relevant for that broad query. Break your target area into ZIP codes or neighborhoods. This single change can increase your total collected listings by 60% or more compared to a broad search.

Using the wrong language setting causes you to miss listings entirely. Business owners set their listing keywords in their local language. Searching in English for businesses in a Spanish-speaking city means you will miss many of them. Match your search language to the local language of the region.

Not adding delays between requests is how you get your IP blocked fast. Google watches for unusually fast, repetitive behavior. Uniform, robotic timing is a red flag. Add random pauses. Vary your scroll speed. Behave like a person, not a machine.

Skipping data validation leads to garbage in your output file. Phone numbers with missing digits, addresses that cut off mid-string, duplicate listings from overlapping searches — all of these appear regularly in scraped data. Build a simple check into your workflow before you import anything into your CRM.

Choosing the wrong tool for your scale costs money and time. Chrome extensions for 5,000 listings, or a paid platform for 30 listings — both are wrong fits. Match the tool to the actual volume you need.

What Does Google Maps Data Look Like When It Is Clean?

Here is a simplified example of what a cleaned export from a Google Maps scrape looks like in practice:

A full export from a dedicated platform includes 40+ columns — social links, GPS coordinates, SEO metadata, ad pixels, working hours. The table above is just the core fields most people start with.

Is It Legal to Scrape Google Maps?

Scraping publicly available data from Google Maps is generally considered legal in the United States. The landmark court case hiQ Labs v. LinkedIn went through the Ninth Circuit and established that scraping public data does not violate the Computer Fraud and Abuse Act. Google Maps data requires no login and no account — it is fully public. That puts it on firmer legal ground than even LinkedIn profiles, which require an account to view.

Google’s Terms of Service technically prohibit automated access. But Google’s own search engine works by crawling and scraping other people’s websites at massive scale every day — so the “no automated collection” clause has always had an internal inconsistency. In practice, millions of businesses use Google Maps scraping tools regularly without any legal consequences. The ones who run into trouble are the ones sending hundreds of thousands of requests per second in ways that visibly disrupt the service.

Here is how the major privacy regulations apply:

The rule of thumb: only collect data that is visible to any browser without logging in. Use tools that manage request rates. Do not harvest personal data. If you are running large-scale commercial operations, talk to a lawyer about your specific jurisdiction.

Tools Worth Knowing About in 2026

Beyond Python, these are the platforms developers and marketers actually use today.

Apify’s Google Maps Scraper has over 907 reviews and a 4.7 average rating. It handles no-code extraction with deep customization options and processes billions of pages monthly across their platform. Reliable, well-maintained, and updated regularly.

Outscraper is cloud-based, works via API or a simple web interface, and supports bulk extraction at country scale. Good for teams who want API access without building infrastructure.

Scrap.io focuses heavily on pre-export filtering — you define your ideal prospect before you pay for any data. It also enriches listings with email, social, and ad pixel data from linked websites.

Bright Data is the enterprise option. Used by large organizations that need massive scale, custom data pipelines, and enterprise-level support. The cost reflects that.

Scrapingdog offers a clean API suite with 5 separate Google Maps endpoints — Search, Reviews, Places, Photos, and Posts. Comes with 1,000 free credits to test. Good for developers who want API-based access without managing browser automation themselves.

Tips to Avoid Getting Blocked While Scraping

Getting blocked is the most frustrating part of DIY scraping. Here are 8 practices that reduce that risk significantly.

• Add random delays between requests (1.5 to 4 seconds works well)
• Vary your scroll speed instead of using a fixed interval
• Rotate your IP address using a proxy service if scraping at high volume
• Use headless browser mode to reduce your visible fingerprint
• Limit your daily request volume when testing a new scraper
• Handle CAPTCHA responses gracefully — stop and retry rather than looping endlessly
• Break large geographic areas into smaller units (ZIP codes, neighborhoods)
• Monitor your data quality actively — sudden empty results often mean you are blocked silently

No technique eliminates the risk entirely. If you are scraping at serious volume, a managed platform with built-in proxy rotation and anti-bot handling is the more reliable long-term choice.

Conclusion

Scraping data from Google Maps is one of the most practical data skills you can build today. Google Maps holds over 200 million business listings, and all of it is publicly visible. The only question is how efficiently you collect it.

If you are a developer, Python with Selenium or Playwright gives you full control. Start small, test your selectors on a sample of 20–30 listings, validate your output, then scale up. Expect to update your code periodically as Google changes its interface.

If you do not write code or just need results fast, dedicated platforms like Apify, Outscraper, and Scrap.io remove the technical complexity entirely. They handle proxy management, anti-bot detection, and data enrichment for you. Most have free trials so you can test your exact use case before spending anything.

The ZIP code strategy is the single biggest improvement most people can make to their scraping workflow right now. Break broad geographic searches into smaller units. You will collect significantly more listings and miss fewer businesses that would have been buried in a broad-area result cap.

Plan for data cleaning from the start. Scraped data always has inconsistencies — missing fields, odd formatting, duplicate entries. Build a normalization step into your process before you push anything into a CRM or analysis tool.

The data is there. Two billion people use Google Maps every month, and the businesses they are searching for are all listed. You do not need weeks of work to access that information. You need the right tool and a clear plan for what you are collecting and why.

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