How to Scrape Google Maps and Local Business Data (2026 Guide)

python3 -m venv maps-scraper
source maps-scraper/bin/activate
pip install curl_cffi selectolax

import re
import json
from curl_cffi import requests
from urllib.parse import quote

def search_google_maps(
    query: str,
    lat: float | None = None,
    lng: float | None = None,
    zoom: int = 14,
    proxy: str | None = None,
) -> list[dict]:
    """Search Google Maps and return a list of business summaries."""
    session = requests.Session(impersonate="chrome")
    proxies = {"https": proxy, "http": proxy} if proxy else None

    # Build the search URL
    encoded_query = quote(query)
    if lat and lng:
        url = f"https://www.google.com/maps/search/{encoded_query}/@{lat},{lng},{zoom}z"
    else:
        url = f"https://www.google.com/maps/search/{encoded_query}/"

    headers = {
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
        "Accept-Language": "en-US,en;q=0.9",
    }

    response = session.get(url, headers=headers, proxies=proxies, timeout=20)
    html = response.text

    # Google Maps embeds business data in a JavaScript variable
    # Look for the data payload in the page source
    businesses = extract_search_results(html)
    return businesses

def extract_search_results(html: str) -> list[dict]:
    """Parse business listings from Google Maps search page HTML."""
    results = []

    # Google Maps embeds structured data in script tags as JSON arrays
    # Find the main data payload
    pattern = r"window\.APP_INITIALIZATION_STATE\s*=\s*(.+?);\s*window\.APP_FLAGS"
    match = re.search(pattern, html, re.DOTALL)
    if not match:
        return results

    raw = match.group(1)
    # The data is a nested array structure — extract business entries
    # Each business has a predictable nested position in the array
    try:
        # Google encodes data as nested arrays — parse carefully
        data_blocks = re.findall(r'\["0x[0-9a-f]+:[0-9a-fx]+".*?\]\]\]', raw)
        for block in data_blocks:
            try:
                parsed = json.loads(f"[{block}]")
                # Extract fields from known positions in the nested array
                if len(parsed) > 0:
                    results.append(parse_business_entry(parsed))
            except (json.JSONDecodeError, IndexError):
                continue
    except Exception:
        pass

    return results

def parse_business_entry(data: list) -> dict:
    """Extract business fields from a parsed Maps data structure."""
    # Positions in Google's nested arrays (these shift occasionally)
    return {
        "name": safe_get(data, [0, 0, 0]) or "",
        "place_id": safe_get(data, [0, 0, 1]) or "",
        "rating": safe_get(data, [0, 0, 2]) or None,
        "review_count": safe_get(data, [0, 0, 3]) or 0,
    }

def safe_get(data, indices, default=None):
    """Safely traverse a nested list structure."""
    current = data
    for idx in indices:
        try:
            current = current[idx]
        except (IndexError, TypeError, KeyError):
            return default
    return current

from dataclasses import dataclass, field
from selectolax.parser import HTMLParser
import re
import json

@dataclass
class BusinessDetail:
    name: str
    address: str
    phone: str
    website: str
    rating: float | None
    review_count: int
    category: str
    hours: dict[str, str]
    latitude: float | None
    longitude: float | None
    price_level: str
    photos_count: int

def get_business_details(
    place_url: str,
    proxy: str | None = None,
) -> BusinessDetail:
    """Fetch full details for a Google Maps business listing."""
    session = requests.Session(impersonate="chrome")
    proxies = {"https": proxy, "http": proxy} if proxy else None

    response = session.get(
        place_url,
        headers={
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
            "Accept-Language": "en-US,en;q=0.9",
        },
        proxies=proxies,
        timeout=20,
    )
    html = response.text
    tree = HTMLParser(html)

    # Extract structured data from JSON-LD (when available)
    json_ld = extract_json_ld(tree)

    # Fall back to parsing the embedded data payload
    embedded = extract_embedded_data(html)

    return BusinessDetail(
        name=json_ld.get("name", embedded.get("name", "")),
        address=json_ld.get("address", {}).get("streetAddress", embedded.get("address", "")),
        phone=embedded.get("phone", ""),
        website=embedded.get("website", ""),
        rating=json_ld.get("aggregateRating", {}).get("ratingValue"),
        review_count=int(json_ld.get("aggregateRating", {}).get("reviewCount", 0)),
        category=embedded.get("category", ""),
        hours=embedded.get("hours", {}),
        latitude=embedded.get("lat"),
        longitude=embedded.get("lng"),
        price_level=embedded.get("price_level", ""),
        photos_count=embedded.get("photos_count", 0),
    )

def extract_json_ld(tree: HTMLParser) -> dict:
    """Extract JSON-LD structured data from the page."""
    for script in tree.css('script[type="application/ld+json"]'):
        try:
            data = json.loads(script.text())
            if isinstance(data, dict) and data.get("@type") == "LocalBusiness":
                return data
            if isinstance(data, list):
                for item in data:
                    if isinstance(item, dict) and item.get("@type") == "LocalBusiness":
                        return item
        except json.JSONDecodeError:
            continue
    return {}

def extract_embedded_data(html: str) -> dict:
    """Extract business data from Google Maps' embedded JavaScript payload."""
    result = {}

    # Phone number pattern
    phone_match = re.search(r'"(\+?1?[\s-]?\(?\d{3}\)?[\s-]?\d{3}[\s-]?\d{4})"', html)
    if phone_match:
        result["phone"] = phone_match.group(1)

    # Website pattern
    website_match = re.search(r'"(https?://(?:www\.)?[a-zA-Z0-9][a-zA-Z0-9.-]+\.[a-zA-Z]{2,})"', html)
    if website_match:
        result["website"] = website_match.group(1)

    # Coordinates
    coord_match = re.search(r'@(-?\d+\.\d+),(-?\d+\.\d+)', html)
    if coord_match:
        result["lat"] = float(coord_match.group(1))
        result["lng"] = float(coord_match.group(2))

    return result

from dataclasses import dataclass
import time
import random

@dataclass
class BoundingBox:
    north: float
    south: float
    east: float
    west: float

def create_grid(bbox: BoundingBox, rows: int = 4, cols: int = 4) -> list[tuple[float, float]]:
    """Divide a bounding box into a grid of center points."""
    lat_step = (bbox.north - bbox.south) / rows
    lng_step = (bbox.east - bbox.west) / cols

    centers = []
    for r in range(rows):
        for c in range(cols):
            lat = bbox.south + (r + 0.5) * lat_step
            lng = bbox.west + (c + 0.5) * lng_step
            centers.append((lat, lng))

    return centers

def scrape_area(
    query: str,
    bbox: BoundingBox,
    grid_size: int = 4,
    proxy_pool: list[str] | None = None,
) -> list[dict]:
    """Scrape all businesses matching a query within a geographic area."""
    centers = create_grid(bbox, rows=grid_size, cols=grid_size)
    all_businesses = {}
    proxy_idx = 0

    for lat, lng in centers:
        proxy = proxy_pool[proxy_idx % len(proxy_pool)] if proxy_pool else None
        proxy_idx += 1

        results = search_google_maps(
            query=query,
            lat=lat,
            lng=lng,
            zoom=15,  # Higher zoom = smaller area, more precise
            proxy=proxy,
        )

        for biz in results:
            # Deduplicate by place_id
            pid = biz.get("place_id", biz.get("name", ""))
            if pid and pid not in all_businesses:
                all_businesses[pid] = biz

        # Respectful delay between requests
        time.sleep(random.uniform(2.0, 5.0))

    return list(all_businesses.values())

# Example: scrape coffee shops in San Francisco
sf_bbox = BoundingBox(
    north=37.8120,
    south=37.7080,
    east=-122.3550,
    west=-122.5150,
)

coffee_shops = scrape_area(
    query="coffee shop",
    bbox=sf_bbox,
    grid_size=5,
    proxy_pool=["http://user:pass@proxy1:8080", "http://user:pass@proxy2:8080"],
)
print(f"Found {len(coffee_shops)} unique coffee shops in SF")

import json
import csv
from dataclasses import asdict
from datetime import date

def export_businesses_csv(businesses: list[BusinessDetail], filepath: str):
    """Export business data to CSV for spreadsheet analysis."""
    if not businesses:
        return

    fieldnames = [
        "name", "address", "phone", "website", "rating",
        "review_count", "category", "latitude", "longitude",
        "price_level", "photos_count", "scraped_at",
    ]

    with open(filepath, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
        for biz in businesses:
            row = asdict(biz)
            row.pop("hours", None)  # Hours are complex — store separately or flatten
            row["scraped_at"] = date.today().isoformat()
            writer.writerow(row)

def export_businesses_json(businesses: list[BusinessDetail], query: str, filepath: str):
    """Export business data as structured JSON."""
    output = {
        "query": query,
        "scraped_at": date.today().isoformat(),
        "total_results": len(businesses),
        "businesses": [asdict(biz) for biz in businesses],
    }
    with open(filepath, "w", encoding="utf-8") as f:
        json.dump(output, f, indent=2, ensure_ascii=False)

def deduplicate_businesses(businesses: list[dict]) -> list[dict]:
    """Remove duplicate businesses based on name + address similarity."""
    seen = set()
    unique = []
    for biz in businesses:
        # Create a fingerprint from normalized name + address
        key = (
            biz.get("name", "").lower().strip(),
            biz.get("address", "").lower().strip()[:30],  # First 30 chars of address
        )
        if key not in seen:
            seen.add(key)
            unique.append(biz)
    return unique