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.
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.
Data Type | Why It Matters |
Name | Identifies the business and serves as a reliable anchor for CRM records and deduplication. |
Address | One of the most valuable fields. Scraping address data from Google Maps supports local lead generation, territory mapping, and geographic segmentation. |
Phone Number | Helps confirm business activity and enables fast outreach and verification. |
Website | Connects the listing to a company’s online presence and supports SEO checks and competitor analysis. |
Ratings & Reviews | Provide insight into customer sentiment and competitive positioning, useful for filtering prospects. |
Coordinates (Lat/Lng) | Useful for mapping, clustering, and understanding regional density. |
Opening Hours | Helps plan outreach timing, assess availability, and identify outdated listings. |
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.
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.
👀 You may also want to know
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 Playwright and Pandas:
pip install playwright pandasThen install Chromium:
playwright install chromiumPlaywright’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.
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 filesStart 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.
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.
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.
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.
Once the left panel is visible, extract the fields shown in each business card and keep scrolling until the list stops expanding.
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 "",
})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 0This 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.
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.
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.
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.
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.
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.
