How to Scrape Google Maps in 2026 with Python (Complete Guide)

You’ve probably tried scraping Google Maps before and realized it isn’t as simple as you expected. Pages reload without warning, results slow down, and captchas appear after only a few scrolls. It’s frustrating when all you want is reliable data for leads, local research, or checking basic business details. Many teams eventually need scraping data at scale for outreach, competitive analysis, or internal research workflows.

Google Maps is one of the most accurate and frequently updated business databases available, but its dynamic loading and behavior checks make simple scraping unreliable. This guide shows you how to scrape Google Maps with Python in 2026 using a step-by-step method built to remain stable with the way Maps loads and verifies traffic today.

What Data You Can Extract From Google Maps

When scraping Google Maps, the goal is usually to build a practical business dataset rather than collect isolated pieces of page content. Google Maps exposes a consistent set of structured business fields that are commonly used in lead generation, CRM enrichment, SEO research, and market analysis.

When you scrape data from Google Maps and normalize these fields, the result becomes a reusable dataset that supports lead generation, CRM workflows, and broader market insight pipelines. This is why scraping Google Maps data has become a standard step in many modern data collection processes.

The Most Reliable Way to Scrape Google Maps: Python + Rotating Residential Proxies

Google Maps relies heavily on client-side rendering and updates parts of the interface as you scroll, which makes selectors unstable and prevents request-based scripts from capturing dynamic content reliably. Since list items and detail panels load only when needed, lightweight scrapers often miss data, slow down, or trigger CAPTCHAs quickly.

Playwright excels here by controlling a real browser. It scrolls incrementally, waits for elements to load, and maintains stable access to both the list and detail panels. This makes it the preferred tool for google map scraping workflows that depend on reliable scrolling, clicking, and handling dynamic content.

Once browser automation is addressed, the primary constraint shifts to IP reputation. Google Maps responds best to clean residential IPs, consistent rotation, and a cost-effective setup for long-running jobs.

IPcook’s residential proxy pool aligns well with these needs:

• Cost‑effective residential IPs that keep extended scraping affordable.

• Rotating IPs for listings and sticky sessions for detail pages, matching how Maps loads data.

• Stable sessions that reduce retries from sudden IP changes.

• Accurate geo‑targeting to maintain local result relevance.

This makes it a practical choice for sustained Google Maps scraping in lead generation and market research pipelines, without over‑engineering or enterprise‑grade expense.

Step 1: Set Up Your Python & Playwright Environment

This step focuses on getting a minimal environment running that can open Google Maps, scroll the page, and wait for elements to load correctly. Once this setup works, the rest of the process to scrape Google Maps can be built on top of it.

Install Required Packages

Install Playwright and Pandas:

pip install playwright pandas

Then install Chromium:

playwright install chromium

Playwright’s managed Chromium build tends to work best with Google Maps because its rendering and JavaScript behavior stay consistent across runs, reducing layout issues and timing errors during scrolling.

Use the following minimal script to confirm the setup:

from playwright.sync_api import sync_playwright

def main():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=False)  # Visible for verification
        page = browser.new_page()
        page.goto("https://www.google.com/maps", wait_until="domcontentloaded")
        page.wait_for_timeout(1500)  # small pause for initial render

        print("Page title:", page.title())
        browser.close()

if __name__ == "__main__":
    main()

If the script opens Google Maps and prints the page title, your environment is ready.

Prepare a Clean Working Folder

Keeping the project organized makes the scraper easier to maintain and extend. A simple folder structure is enough:

google-maps-scraper/
  main.py          # Controls the overall workflow
  scrolling.py     # Handles scrolling behavior and stop conditions
  selectors.py     # Extracts business fields
  export.py        # Saves CSV/JSON output
  data/            # Stores output files

Start main.py with a basic skeleton:

def run():
    # 1. Open Google Maps
    # 2. Load search results for a query
    # 3. Scroll the results list
    # 4. Open detail pages and extract fields
    # 5. Export structured data
    pass

if __name__ == "__main__":
    run()

This structure keeps each part of the scraper focused on a single responsibility, making it easier to debug and expand as you add logic to scrape Google Maps.

Step 2: Add Rotating Residential Proxies to Avoid Blocks

With the browser environment ready, IP reputation becomes the main limit on your scraper’s run time. Google Maps monitors repeated scrolling, navigation, and detail-page visits. Too many actions from one IP can slow the interface, hide elements, or trigger CAPTCHAs. For sustained google map scraping, residential proxies distribute activity across cleaner IPs to keep the workflow stable.

Configure Proxy Settings in Playwright

Playwright applies proxies at the browser level, ensuring JavaScript, map tiles, and background requests all share the same IP. If you do not have a residential proxy yet, you can generate one directly in the IPcook dashboard.

A basic setup looks like this:

from playwright.sync_api import sync_playwright

# Replace with your proxy details:
PROXY_SERVER = "http://USERNAME:[email protected]:PORT"

def main():
    with sync_playwright() as p:
        browser = p.chromium.launch(
            proxy={"server": PROXY_SERVER},
            headless=False  # Set to True for production
        )
        page = browser.new_page()
        page.goto("https://www.google.com/maps", wait_until="domcontentloaded")
        page.wait_for_timeout(1500)  # Allow initial render

        print("Page title:", page.title())
        browser.close()

if __name__ == "__main__":
    main()

Geo-matched residential IPs from IPcook also help keep local results consistent during regional data collection.

Step 3: Load Google Maps Search Results for Your Target Keywords

Start by building a Google Maps search URL from your keyword and city, then load it in Playwright.

query = "coffee shops in Seattle"
search_url = f"https://www.google.com/maps/search/{query.replace(' ', '+')}"
page.goto(search_url, wait_until="domcontentloaded")
page.wait_for_timeout(2000)

If your query includes special characters, encode it before generating the URL. Once the page opens, wait until the results panel on the left is visible. The next step will handle scrolling and extraction together, so you only need a clean, loaded starting state here.

Step 4: Extract Business List Data From the Left Panel

Once the left panel is visible, extract the fields shown in each business card and keep scrolling until the list stops expanding.

Selectors for Name, Rating, Category

Google Maps uses dynamic class names, so selectors should rely on stable roles and structure. Treat each result card as a container and extract fields relative to it.

A simple example looks like this:

cards = page.query_selector_all("div[role='article']")
list_results = []

for card in cards:
    name_el = card.query_selector("h3")
    rating_el = card.query_selector("span[aria-label*='stars']")
    category_el = card.query_selector("span[jsinstance]")  # layout-dependent

    list_results.append({
        "name": name_el.inner_text().strip() if name_el else "",
        "rating": rating_el.get_attribute("aria-label") if rating_el else "",
        "category": category_el.inner_text().strip() if category_el else "",
    })

Automate Scrolling to Load More Results

Scroll in small increments, pause briefly, and stop after a few rounds where no new businesses appear. Track a simple key, such as the name, to avoid duplicates.

seen = set()
results = []
stable_rounds = 0
max_rounds = 3

while stable_rounds < max_rounds:
    cards = page.query_selector_all("div[role='article']")
    before = len(seen)

    for card in cards:
        name_el = card.query_selector("h3")
        name = name_el.inner_text().strip() if name_el else None

        if name and name not in seen:
            seen.add(name)
            # extract rating/category exactly as above
            results.append({"name": name})

    page.mouse.wheel(0, 800)
    page.wait_for_timeout(1500)

    stable_rounds = stable_rounds + 1 if len(seen) == before else 0

This pattern loads the remaining cards at a steady pace, avoids duplicates, and stops once the panel no longer expands. At this point, the list-level dataset is ready for the next step, where each business profile is opened to extract full details. Duplicates are common in Maps, so a stricter key like name plus address works better once you extract addresses in the next step.

Step 5: Open Each Business Profile and Extract Full Details

With the list-level data collected, the next step is to open each business profile and extract fields that only appear in the detail panel.

Click-Through Navigation (Handling Delays)

Each profile loads inside a detail panel after a click, and render speed can vary. To avoid missing fields, open one listing at a time and wait for a detail-only element before extracting data.

card.click()
page.wait_for_timeout(800)

page.wait_for_selector(
    "a[href^='tel:'], button[data-item-id^='phone:tel'], a[data-item-id='authority'], div[aria-label*='Hours']",
    timeout=5000
)

If the selector does not appear within the timeout, skip the listing and continue. This prevents blocking on slow or partially rendered profiles.

Extract Phone, Website, Hours, Coordinates

Once the detail panel is ready, extract the remaining fields.

Phone and website are usually exposed as links:

phone_el = page.query_selector("a[href^='tel:']")
phone = phone_el.get_attribute("href").replace("tel:", "") if phone_el else ""

website_el = page.query_selector("a[data-item-id='authority'], a[href^='http']")
website = website_el.get_attribute("href") if website_el else ""

Opening hours often vary in layout, so capturing raw text is usually more reliable:

hours_el = page.query_selector("div[aria-label*='Hours']")
hours = hours_el.inner_text().strip() if hours_el else ""

Coordinates can be extracted in two ways. For most scraping google maps data workflows, reading them from the page URL offers the best balance of speed and simplicity. Parsing embedded page data is more consistent but requires additional processing.

# Method 1: From the page URL
current_url = page.url

# Method 2: From embedded page data
script_content = page.content()

Store all fields in a single record to keep data aligned:

detail_record = {
    "name": name,
    "rating": rating,
    "category": category,
    "phone": phone,
    "website": website,
    "hours": hours,
    "latitude": lat,
    "longitude": lng,
}

At this point, each listing has been converted into structured data, and the scraper is ready for cleaning and export.

Step 6: Clean, Structure, and Export Your Google Maps Dataset

After extraction, clean and structure the dataset to make it reliable and ready for use. Remove duplicates using name and address or coordinates, handle missing values explicitly, and normalize addresses and phone numbers to keep records consistent.

Export the final dataset as CSV for CRM imports or JSON for automation pipelines. At this stage, the scraping google maps data workflow produces a reusable, structured dataset rather than a one-time output.

Conclusion

This guide showed a complete, working approach to scrape Google Maps in 2026 using Python. By combining Playwright for dynamic rendering, controlled scrolling for result loading, and residential proxies for stability, you can build a google map scraping workflow that runs reliably beyond short tests. IPcook fits this model well, allowing teams to move from small tests to sustained data collection without a large upfront cost. esidential traffic priced as low as $0.5 per GB makes long-running scraping jobs feasible without turning IP usage into a bottleneck.