DEV Community: GridForge

Track hotel prices for any city into a spreadsheet with Python

GridForge — Thu, 29 Oct 2026 17:48:34 +0000

Comparing hotel prices gets tedious when you keep repeating the same search and copying rates into a spreadsheet. Tracking how those prices change is even harder unless you save the dates, guest count and time of each search.

I built the tool used here.

This walkthrough uses GridForge's Google Hotels Scraper to collect a hotel-price snapshot and export it to CSV. You can start without code, then use Python when you want a repeatable workflow.

What you get

Each result represents one hotel from the first page of the location search. Available fields include:

Field	What it tells you
`name`	Hotel name
`pricePerNight`	Displayed nightly price before taxes
`pricePerNightWithTaxes`	Displayed nightly price including taxes
`rating`	Guest rating
`reviewCount`	Number of guest reviews
`hotelClass`	Hotel star classification
`latitude`, `longitude`	Property coordinates
`website`	Official website, when supplied

Guest rating and hotel class measure different things. A four-star hotel does not necessarily have a guest rating of four. Some properties may have missing fields; keep those cells empty instead of treating a missing price as zero.

Option 1: no code

Open the Google Hotels Scraper and click Try for free.
Enter a city, neighborhood or area, such as Times Square, New York.
Set check-in and check-out dates. Choose the number of adults and currency.
Click Start.
Open the resulting dataset, choose Export, and download CSV or Excel.

For your first search, use one location and one stay. A neighborhood query is useful when a whole-city search gives you properties spread too far apart.

Option 2: Python

pip install apify-client

Set your Apify token in an environment variable named APIFY_TOKEN. This script requests one stay and writes only the columns needed for a price-comparison spreadsheet:

import csv
import os
from datetime import datetime, timezone
from decimal import Decimal

from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

search = {
    "locations": ["Times Square, New York"],
    "checkIn": "2027-01-15",
    "checkOut": "2027-01-18",
    "adults": 2,
    "currency": "USD",
    "country": "us",
}

run = client.actor("gridforge/google-hotels-scraper").call(
    run_input=search,
    max_total_charge_usd=Decimal("0.05"),  # hard spending cap for this run
)

if not run or run.get("status") != "SUCCEEDED":
    raise RuntimeError("Hotel search did not succeed")

checked_at = datetime.now(timezone.utc).isoformat()
columns = ["checkedAt", "checkIn", "checkOut", "currency", "name",
           "pricePerNight", "pricePerNightWithTaxes", "rating", "reviewCount", "website"]

with open("hotel-prices.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=columns)
    writer.writeheader()
    for hotel in client.dataset(run["defaultDatasetId"]).iterate_items():
        if not hotel.get("name"):
            print("Check this record:", hotel)
            continue
        row = {key: hotel.get(key) for key in columns}
        row.update({"checkedAt": checked_at, "checkIn": search["checkIn"],
                    "checkOut": search["checkOut"], "currency": search["currency"]})
        writer.writerow(row)

Replace the example dates with your intended stay. The script records the requested dates and currency beside each result, so every snapshot is easy to interpret later.

Use case: daily price tracking

Choosing a hotel for a three-night trip? Run the same search daily, keeping the stay dates, adult count, currency and location unchanged. Append each run to one spreadsheet and compare the tax-inclusive price for the same property across days. Once your first results look right, set up a daily Schedule in Apify so it runs on its own.

Limits

Returns the first page Google shows, typically around 18–20 hotels per location. Search by neighborhood for more coverage.
Displayed rates change and may differ from the final price at checkout.
Nearby searches can overlap; deduplicate before counting unique hotels.

Cost: $1 per 1,000 hotels, plus a tiny start fee. Set a spending cap and a run can never cost more than you allow.

Not affiliated with or endorsed by Google.

Export Google News results to CSV with Python in 5 minutes

GridForge — Thu, 29 Oct 2026 16:59:32 +0000

Google News has no official API. If you want news for a keyword in a spreadsheet, for brand monitoring, a weekly PR report, competitor tracking, or feeding an AI agent, you usually end up copy-pasting.

This tutorial gets you a clean CSV in about 5 minutes. Disclosure: I built the tool used here (GridForge Google News Scraper on Apify). It reads Google News' public RSS feeds, so there's no browser automation and nothing to break when the page layout changes.

What you get

One row per article:

Field	Example
`query`	`site:techcrunch.com startups when:7d`
`title`	Example headline - TechCrunch
`publisher`	TechCrunch
`publisherUrl`	https://techcrunch.com/
`publishedAt`	2026-10-29T14:06:20+00:00
`googleNewsUrl`	https://news.google.com/rss/articles/...

Duplicates across your searches are removed. Google News search operators work: site:, when:7d, "exact phrase", -exclude.

Option 1: no code

Open the tool page and click Try for free.
Type your searches, one per line.
Click Start, then Export to CSV or Excel.

Apify's free plan includes monthly credits, and the tool costs $2 per 1,000 articles, so small monitoring jobs fit inside the free credits.

Option 2: Python

pip install apify-client

Grab your API token from Apify (Settings → API & Integrations) and set it as APIFY_TOKEN.

import csv, os
from decimal import Decimal
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

run = client.actor("gridforge/google-news-scraper").call(
    run_input={
        "queries": ["artificial intelligence when:7d", "site:techcrunch.com startups when:7d"],
        "language": "en",
        "country": "US",
        "maxArticlesPerQuery": 20,
    },
    max_total_charge_usd=Decimal("0.10"),  # hard spending cap for this run
)

rows = [r for r in client.dataset(run["defaultDatasetId"]).iterate_items() if r.get("status") == "ok"]

with open("news.csv", "w", newline="", encoding="utf-8") as f:
    w = csv.DictWriter(f, fieldnames=["query", "title", "publisher", "publishedAt", "googleNewsUrl", "publisherUrl"], extrasaction="ignore")
    w.writeheader()
    w.writerows(rows)

print(f"Saved {len(rows)} articles to news.csv")

That's it. Open news.csv in Excel or Google Sheets.

Make it a daily monitor

In the Apify console, open the tool → Schedules → run it every morning with the same input. Each run's results land in a new dataset you can pull with the same script, or send to Google Sheets, Slack or a webhook through Apify integrations or Zapier/Make/n8n.

Limits worth knowing

Google's feed returns up to about 100 articles per search. For more coverage, split into narrower searches (by site, phrase or when:1d).
You get headline, publisher, date and link, not full article text.
Results reflect what Google News shows at run time.

Cost

$2 per 1,000 articles plus a tiny per-run start fee. Searches that fail aren't charged per article. Set max_total_charge_usd (or the spending limit in the console) and a run can never cost more than you allow.

Questions or a field you need? Leave a comment and I'll take a look.

Not affiliated with or endorsed by Google.