Eliminar Google Maps directamente es frágil y corre el riesgo de prohibiciones de propiedad intelectual. Los datos de Google Maps aparecen en los resultados de búsqueda como listados de paquetes locales con el nombre de la empresa, calificación, dirección, teléfono y horario. Al buscar a través de la API de Scavio a $0,005 por solicitud, obtiene datos comerciales locales estructurados sin administrar servidores proxy, navegadores sin cabeza ni solucionadores de CAPTCHA. Este tutorial crea un extractor de datos comerciales locales que extrae datos con calidad de Maps de los resultados de búsqueda.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
- Una lista de categorías de negocios o ubicaciones para buscar
Guia paso a paso
Paso 1: Busque empresas locales a través de la API de búsqueda
Consulta empresas locales y extrae los resultados del paquete local. Estos contienen los mismos datos que obtendría de Google Maps: nombre, calificación, dirección y más.
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')}")Paso 2: Extraer datos comerciales estructurados
Analice los resultados de la búsqueda para extraer registros comerciales limpios. Combine datos de paquetes locales con fragmentos de resultados orgánicos para obtener perfiles más completos.
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 ''}")Paso 3: Extracto por lotes en múltiples categorías
Busque varias categorías de empresas en una ubicación para crear una base de datos de empresas locales completa. Límite de tarifa para mantenerse dentro de las pautas de API.
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}')Paso 4: Exportar a CSV para análisis
Guarde los datos comerciales extraídos en CSV para usarlos en hojas de cálculo, importaciones de CRM o análisis adicionales.
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')Ejemplo en Python
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')Ejemplo en JavaScript
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');Salida esperada
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