Scraping Google Maps directly is fragile and risks IP bans. Google Maps data appears in search results as local pack listings with business name, rating, address, phone, and hours. By searching through the Scavio API at $0.005 per request, you get structured local business data without managing proxies, headless browsers, or CAPTCHA solvers. This tutorial builds a local business data extractor that pulls Maps-quality data from search results.
Prerequisites
- Python 3.9+ installed
- requests library installed
- A Scavio API key from scavio.dev
- A list of business categories or locations to search
Walkthrough
Step 1: Search for local businesses via the search API
Query for local businesses and extract the local pack results. These contain the same data you would scrape from Google Maps: name, rating, address, and more.
Python
import os, requests, json
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
URL = 'https://api.scavio.dev/api/v1/search'
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def search_local_businesses(query: str, location: str = 'us') -> dict:
"""Search for local businesses and extract structured data."""
resp = requests.post(URL, headers=H,
json={'query': query, 'country_code': location, 'num_results': 10})
resp.raise_for_status()
data = resp.json()
return {
'local_results': data.get('local_results', []),
'organic_results': data.get('organic_results', []),
'knowledge_graph': data.get('knowledge_graph', {}),
}
result = search_local_businesses('best coffee shops in Austin TX')
local = result['local_results']
print(f'Found {len(local)} local results')
for biz in local[:5]:
print(f" {biz.get('title', 'N/A')}")
print(f" Rating: {biz.get('rating', 'N/A')} ({biz.get('reviews', 'N/A')} reviews)")
print(f" Address: {biz.get('address', 'N/A')}")Step 2: Extract structured business data
Parse the search results to extract clean business records. Combine local pack data with organic result snippets for richer profiles.
Python
def extract_business_data(query: str, location: str = 'us') -> list:
"""Extract structured business records from search results."""
data = search_local_businesses(query, location)
businesses = []
# Extract from local results (Maps data)
for biz in data.get('local_results', []):
businesses.append({
'name': biz.get('title', ''),
'rating': biz.get('rating', None),
'reviews_count': biz.get('reviews', None),
'address': biz.get('address', ''),
'phone': biz.get('phone', ''),
'hours': biz.get('hours', ''),
'type': biz.get('type', ''),
'source': 'local_pack',
})
# Extract from organic results
for result in data.get('organic_results', []):
snippet = result.get('snippet', '')
rich = result.get('rich_snippet', {})
if rich:
businesses.append({
'name': result.get('title', ''),
'rating': rich.get('rating', None),
'reviews_count': rich.get('reviews', None),
'address': '',
'phone': '',
'url': result.get('link', ''),
'source': 'organic_rich',
})
return businesses
businesses = extract_business_data('plumbers in Denver CO')
print(f'Extracted {len(businesses)} businesses')
for b in businesses[:5]:
print(f" {b['name']} - Rating: {b['rating']} ({b['source']})"
f"{' ' + b['address'] if b['address'] else ''}")Step 3: Batch extract across multiple categories
Search multiple business categories in a location to build a comprehensive local business database. Rate limit to stay within API guidelines.
Python
import time
def batch_extract(categories: list, location: str, city: str) -> list:
"""Extract businesses across multiple categories."""
all_businesses = []
for category in categories:
query = f'{category} in {city}'
print(f'Searching: {query}')
businesses = extract_business_data(query, location)
for b in businesses:
b['category'] = category
b['city'] = city
all_businesses.extend(businesses)
time.sleep(0.5) # Rate limiting
# Deduplicate by name
seen = set()
unique = []
for b in all_businesses:
key = b['name'].lower().strip()
if key and key not in seen:
seen.add(key)
unique.append(b)
return unique
categories = ['restaurants', 'dentists', 'auto repair', 'hair salons']
businesses = batch_extract(categories, 'us', 'Portland OR')
print(f'\nTotal unique businesses: {len(businesses)}')
print(f'Cost: {len(categories)} searches = ${len(categories) * 0.005:.3f}')
for cat in categories:
count = len([b for b in businesses if b.get('category') == cat])
print(f' {cat}: {count}')Step 4: Export to CSV for analysis
Save the extracted business data to CSV for use in spreadsheets, CRM imports, or further analysis.
Python
import csv
def export_businesses(businesses: list, filename: str = 'local_businesses.csv'):
if not businesses:
print('No businesses to export')
return
fieldnames = ['name', 'category', 'city', 'rating', 'reviews_count',
'address', 'phone', 'hours', 'type', 'source']
with open(filename, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction='ignore')
writer.writeheader()
writer.writerows(businesses)
# Summary stats
rated = [b for b in businesses if b.get('rating')]
avg_rating = sum(float(b['rating']) for b in rated) / len(rated) if rated else 0
print(f'Exported {len(businesses)} businesses to {filename}')
print(f' With ratings: {len(rated)}')
print(f' Average rating: {avg_rating:.1f}')
print(f' Categories: {len(set(b.get("category","") for b in businesses))}')
export_businesses(businesses, 'portland_businesses.csv')Python Example
Python
import os, requests, csv, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def get_local_businesses(query):
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': query, 'country_code': 'us', 'num_results': 10})
return resp.json().get('local_results', [])
def extract_and_export(categories, city, output='businesses.csv'):
all_biz = []
for cat in categories:
results = get_local_businesses(f'{cat} in {city}')
for r in results:
all_biz.append({'name': r.get('title',''), 'category': cat,
'rating': r.get('rating',''), 'address': r.get('address','')})
time.sleep(0.3)
with open(output, 'w', newline='') as f:
w = csv.DictWriter(f, fieldnames=['name','category','rating','address'])
w.writeheader()
w.writerows(all_biz)
print(f'Exported {len(all_biz)} businesses')
extract_and_export(['restaurants', 'dentists'], 'Austin TX')JavaScript Example
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function getLocalBusinesses(query) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query, country_code: 'us', num_results: 10 })
});
return (await resp.json()).local_results || [];
}
async function extractBusinesses(categories, city) {
const all = [];
for (const cat of categories) {
const results = await getLocalBusinesses(`${cat} in ${city}`);
results.forEach(r => all.push({ name: r.title, category: cat,
rating: r.rating, address: r.address }));
}
console.log(`Found ${all.length} businesses`);
all.forEach(b => console.log(` ${b.name} (${b.rating}) - ${b.category}`));
}
extractBusinesses(['restaurants', 'dentists'], 'Austin TX');Expected Output
JSON
Found 8 local results
Houndstooth Coffee
Rating: 4.6 (342 reviews)
Address: 401 Congress Ave, Austin, TX
Merit Coffee
Rating: 4.7 (289 reviews)
Address: 222 W 2nd St, Austin, TX
Total unique businesses: 24
Cost: 4 searches = $0.020
restaurants: 8
dentists: 6
auto repair: 5
hair salons: 5
Exported 24 businesses to portland_businesses.csv