Puede extraer datos de empresas locales del paquete local de Google buscando servicio + ubicación a través de la API de Scavio y analizando el campo local_results, que devuelve listados de empresas estructuradas sin raspado.
Requisitos previos
- Python 3.9+
- Clave API de Scavio
Guia paso a paso
Paso 1: Buscar empresas locales
Incluya la ubicación en la consulta. La API devuelve una matriz local_results con datos comerciales estructurados.
Python
import requests
API_KEY = "your-scavio-api-key"
def search_local(service: str, location: str, num_results: int = 10) -> dict:
query = f"{service} in {location}"
r = requests.post(
"https://api.scavio.dev/api/v1/search",
json={"query": query, "num_results": num_results},
headers={"x-api-key": API_KEY},
timeout=20
)
r.raise_for_status()
return r.json()
data = search_local("plumbers", "Austin TX")
local_results = data.get("local_results", [])
print(f"Found {len(local_results)} local businesses")
for biz in local_results:
print(biz.get("title"), biz.get("phone"), biz.get("rating"))Paso 2: Analizar campos de clientes potenciales estructurados
Extraiga todos los campos comerciales disponibles de los resultados del paquete local.
Python
def extract_leads(local_results: list) -> list:
leads = []
for biz in local_results:
leads.append({
"name": biz.get("title"),
"address": biz.get("address"),
"phone": biz.get("phone"),
"website": biz.get("website"),
"rating": biz.get("rating"),
"reviews": biz.get("reviews"),
"hours": biz.get("hours"),
"category": biz.get("type"),
"maps_url": biz.get("link")
})
return leadsPaso 3: Exportar a CSV
Escriba la lista de clientes potenciales en un archivo CSV para usarla en un CRM o una herramienta de divulgación.
Python
import csv
def export_leads_csv(leads: list, filename: str):
if not leads:
print("No leads to export")
return
fields = ["name", "address", "phone", "website", "rating", "reviews", "category"]
with open(filename, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")
writer.writeheader()
writer.writerows(leads)
print(f"Exported {len(leads)} leads to {filename}")
# Full run
data = search_local("plumbers", "Austin TX")
leads = extract_leads(data.get("local_results", []))
export_leads_csv(leads, "austin_plumbers.csv")Paso 4: Cree una lista de clientes potenciales de varias ciudades
Recorra ciudades y servicios para generar una gran base de datos de clientes potenciales.
Python
SERVICES = ["plumbers", "electricians", "HVAC companies"]
CITIES = ["Austin TX", "Dallas TX", "Houston TX"]
all_leads = []
for service in SERVICES:
for city in CITIES:
data = search_local(service, city)
leads = extract_leads(data.get("local_results", []))
for lead in leads:
lead["service"] = service
lead["city"] = city
all_leads.extend(leads)
print(f"{service} in {city}: {len(leads)} leads")
print(f"\nTotal leads: {len(all_leads)}")
export_leads_csv(all_leads, "home_services_leads.csv")Ejemplo en Python
Python
import requests
import csv
from datetime import date
API_KEY = "your-scavio-api-key"
def search_local(service: str, location: str) -> list:
r = requests.post(
"https://api.scavio.dev/api/v1/search",
json={"query": f"{service} in {location}", "num_results": 10},
headers={"x-api-key": API_KEY},
timeout=20
)
r.raise_for_status()
return r.json().get("local_results", [])
def build_lead_list(services: list, cities: list) -> list:
leads = []
for service in services:
for city in cities:
results = search_local(service, city)
for biz in results:
leads.append({
"name": biz.get("title"),
"address": biz.get("address"),
"phone": biz.get("phone"),
"website": biz.get("website"),
"rating": biz.get("rating"),
"reviews": biz.get("reviews"),
"service": service,
"city": city
})
print(f"{service} / {city}: {len(results)} leads")
return leads
def export_csv(leads: list, path: str):
fields = ["name","address","phone","website","rating","reviews","service","city"]
with open(path, "w", newline="", encoding="utf-8") as f:
w = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")
w.writeheader()
w.writerows(leads)
if __name__ == "__main__":
leads = build_lead_list(
services=["plumbers", "electricians"],
cities=["Austin TX", "Dallas TX"]
)
path = f"leads_{date.today()}.csv"
export_csv(leads, path)
print(f"\nExported {len(leads)} leads to {path}")Ejemplo en JavaScript
JavaScript
const API_KEY = 'your-scavio-api-key';
async function searchLocal(service, location) {
const res = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'x-api-key': API_KEY },
body: JSON.stringify({ query: `${service} in ${location}`, num_results: 10 })
});
const data = await res.json();
return data.local_results ?? [];
}
const leads = await searchLocal('plumbers', 'Austin TX');
for (const biz of leads) {
console.log(`${biz.title} | ${biz.phone} | Rating: ${biz.rating} (${biz.reviews})`);
}Salida esperada
JSON
plumbers / Austin TX: 8 leads
plumbers / Dallas TX: 10 leads
electricians / Austin TX: 9 leads
electricians / Dallas TX: 10 leads
Exported 37 leads to leads_2026-05-22.csv
Sample row:
Austin Plumbing Co | 512-555-0199 | 123 Main St Austin TX | Rating: 4.8 (342 reviews)