How to Scrape Google Maps Using Python with ProxyTee
In today’s data-driven world, scraping public web data has become crucial for many businesses. Google Maps, with its wealth of information, is a popular target for web scraping. This post will guide you through the process of scraping Google Maps data using Python and highlight how ProxyTee can enhance your scraping efforts.
Why Scrape Google Maps?
Google Maps provides valuable data for various purposes:
- Research: Analyze demographic information, transportation routes, and urban planning data.
- Business Analysis: Gather data on competitors, including locations, customer reviews, and ratings.
- Real Estate: Collect property listings, pricing information, and neighborhood data.
As a result, scraping Google Maps data becomes a powerful tool for businesses to gain valuable insights.
Limitations of the Official Google Maps API
While Google offers an official API, it comes with limitations:
- Cost: While a monthly credit is available, usage beyond it can become extremely expensive, especially with high request volumes.
- Rate Limits: Google enforces strict request limits (e.g., 100 requests per second), which can hinder large-scale data collection.
- Changes: Google may implement unpredictable changes that affect users.
These constraints often make using dedicated solutions preferable.
Benefits of ProxyTee for Google Maps Scraping
When scraping Google Maps, it’s vital to avoid detection and IP bans. ProxyTee provides the perfect solution, offering:
- Unlimited Bandwidth: No concerns about data overages during extensive scraping tasks.
- Global IP Coverage: Over 20 million IP addresses from more than 100 countries, allowing geo-targeting for specific regions.
- Multiple Protocol Support: Supports HTTP and SOCKS5, ensuring compatibility with scraping tools.
- Auto Rotation: Automatic IP rotation at intervals of 3 to 60 minutes, crucial for preventing IP bans.
- API Integration: Easily integrate with your applications via our simple API.
- Affordable Pricing: ProxyTee offers a cost-effective solution compared to competitors, especially our Unlimited Residential Proxies plan. With a massive IP pool and residential IPs that are rotating, you can be sure your project is not limited in scale or performance. Our Unlimited Residential Proxies are up to 50% cheaper than other providers.
Choosing ProxyTee means reliable, affordable, and scalable web scraping solutions.
How to Scrape Google Maps Using Python
1️⃣ Setup
To get started, ensure you have Python 3.8 or newer installed, along with necessary libraries like beautifulsoup4, lxml, requests, and pandas.
$ python3 -m venv env
# Activate on macOS/Linux
$ source env/bin/activate
# Activate on Windows
$ env\Scripts\activate
$ pip install beautifulsoup4 requests pandas lxml2️⃣ Fetching Data
While this example will demonstrate without dedicated scraping API, you can integrate your scraping code with ProxyTee to ensure your requests are proxied effectively to avoid rate limiting, and IP bans.
This involves sending HTTP requests to Google Maps with specified parameters using requests.
import requests, lxml.html, re, pandas as pd
from bs4 import BeautifulSoup
payload = {
'source': 'google_maps',
'query': 'restaurants near me',
'user_agent_type': 'desktop',
'domain': 'com',
'geo_location': 'New York,United States',
'start_page': '1',
'pages': '3'
}
response = requests.post(
'https://example.com/api',
auth=('USERNAME', 'PASSWORD'),
json=payload,
timeout=180
)
results = response.json()['results']
html_files = [result['content'] for result in results]
3️⃣ Parsing Data
Once you fetch the HTML content, you can parse it using BeautifulSoup and extract required details such as restaurant names, ratings, addresses, hours, etc.
data = []
for html in html_files:
soup = BeautifulSoup(html, 'html.parser')
lxml_obj = lxml.html.fromstring(str(soup))
index = -1
for listing in soup.select('[class="VkpGBb"]'):
index += 1
place = listing.parent
name_el = place.select_one('[role="heading"]')
name = name_el.text.strip() if name_el else ''
rating_el = place.select_one('span[aria-hidden="true"]')
rating = rating_el.text.strip() if rating_el else ''
rating_count_el = place.select_one('[class*="RDApEe"]')
rating_count = ''
if rating_count_el:
count_match = re.search(r'\((.+)\)', rating_count_el.text)
rating_count = count_match.group(1) if count_match else ''
hours_el = place.select_one('.rllt__details div:nth-of-type(4)')
hours = hours_el.text.strip() if hours_el else ''
if 'opens' not in hours.lower():
hours = ''
details_el = place.select_one('.rllt__details div:nth-of-type(5)')
details = details_el.text.strip() if details_el else ''
price_level_el = place.select_one('.rllt__details div:nth-of-type(2) > span:nth-of-type(2)')
price_level = price_level_el.text.strip() if price_level_el else ''
lat_el = soup.select_one('[data-lat]')
lat = lat_el.get('data-lat') if lat_el else ''
lng_el = soup.select_one('[data-lng]')
lng = lng_el.get('data-lng') if lng_el else ''
type_el = lxml_obj.xpath('//div[@class="rllt__details"]/div[2]/text()')
place_types = []
for item in type_el:
parts = item.strip().split('·')
non_empty_parts = [part.strip() for part in parts if part.strip()]
if non_empty_parts:
place_types.append(non_empty_parts[-1])
address_el = place.select_one('.rllt__details div:nth-of-type(3)')
address = address_el.text.strip() if address_el else ''
place = {
'name': name,
'place_type': place_types[index],
'address': address,
'rating': rating,
'price_level': price_level,
'rating_count': rating_count,
'latitude': lat,
'longitude': lng,
'hours': hours,
'details': details,
}
data.append(place)
4️⃣ Exporting Data to CSV
After extracting the data, use pandas to save it into a CSV file.
df = pd.DataFrame(data)
df.to_csv("data.csv", index=False)
