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Web Scraping to Google Sheets: Two Working Methods

Pim · Scrappey Research

June 8, 2026 4 min read

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To get scraped data into Google Sheets you either write rows from code with the gspread library and a Google service account, or pull a published feed into a cell with the built-in IMPORTDATA / IMPORTHTML functions. The code path gives you full control over authentication, multiple tabs, formatting, and scheduled refreshes; the no-code path is fastest when your data already lives at a public URL as CSV or a plain HTML table. Either way the hard part is usually the scrape itself - JavaScript-rendered tables and browser verification pages return empty rows to naive fetchers - so a scraping API that handles rendering and proxies in one call feeds Sheets the cleanest data.

Two paths	Python + gspread + service account (full control), or Apps Script / IMPORTDATA (no-code)
Auth (code path)	Google Cloud service account JSON key; share the Sheet with the account email
Best for	Recurring scrapes, dashboards, and team-shared data without a database
IMPORTDATA limit	Reads only public CSV/TSV at a URL; no JS rendering, ~50 IMPORT functions per sheet
Cleanest input	Scrappey returns JSON or autoparsed tables you can write straight to rows

Method 1: Python with gspread and a service account

The most reliable path is to scrape with an API, shape the result into rows, and write them with gspread authenticated by a Google service account. Set up auth once: in Google Cloud, create a service account, enable the Google Sheets API and Google Drive API, download the JSON key, then open your Sheet and share it (Editor) with the service account email found in that JSON. After that, gspread opens the spreadsheet by key and writes a 2D list in a single batched update() call, which is far faster and friendlier to API quotas than cell-by-cell writes.

Batch, do not loop: build one list of rows and send it once; avoid a update_cell() call per field.
Multiple tabs: use worksheet() or add_worksheet() to split datasets across sheets in the same workbook.
Append vs overwrite: append_rows() adds to the bottom for incremental runs; clear then write for full refreshes.
Headers: write a header row first so downstream formulas and pivots have named columns.

Method 2: No-code with IMPORTDATA, IMPORTHTML, and Apps Script

If your data is already a public CSV or a plain HTML table, you can pull it into a cell with no code at all. =IMPORTDATA("https://example.com/data.csv") loads a public CSV or TSV; =IMPORTHTML("https://example.com/page","table",1) grabs the first HTML table on a page. These are great for simple, static feeds but they have real limits: they cannot run JavaScript, cannot send custom headers or handle browser verification, and a sheet is capped at roughly 50 IMPORT-family functions. For anything dynamic, write a Google Apps Script function that calls a scraping API with UrlFetchApp.fetch(), parses the JSON, and writes rows with getRange().setValues(). Bind that function to a time-driven trigger to refresh on a schedule directly inside Sheets.

Get clean data in first: handle JS and verification

Both methods only work if the scrape returns real data, and that is where most Sheets tutorials quietly fail. Power Query, IMPORTHTML, and a plain requests.get() all return an empty preview when a site renders its table with JavaScript or shows a browser verification interstitial. A scraping API solves this by rendering the page in a real browser and routing through residential proxies, then handing you the finished HTML or an autoparsed table. With Scrappey you send one POST, set autoparse: true to get structured rows back, and reuse a session so paginated requests share cookies. Loop through pages, collect rows into one list, and write that list to Google Sheets in a single batched call - one workbook, every page, no empty previews.

Code example

python

import requests
import gspread

# 1) Scrape with Scrappey (handles JS rendering + proxies in one call)
API_KEY = "YOUR_API_KEY"
resp = requests.post(
    "https://publisher.scrappey.com/api/v1?key=" + API_KEY,
    json={
        "cmd": "request.get",
        "url": "https://example.com/products",
        "proxyCountry": "UnitedStates",
        "session": "sheets-job",
        "autoparse": True,
    },
    timeout=180,
)
resp.raise_for_status()
solution = resp.json()["solution"]

# autoparse returns structured data; fall back to raw HTML if you parse yourself
items = solution.get("parsed") or []

# 2) Shape into rows (header first)
rows = [["name", "price", "url"]]
for it in items:
    rows.append([it.get("name", ""), it.get("price", ""), it.get("url", "")])

# 3) Authenticate to Google with a service account JSON key.
# In Google Cloud: enable Sheets API + Drive API, create a service account,
# download the key, then share the Sheet (Editor) with the account email.
gc = gspread.service_account(filename="service_account.json")
sh = gc.open_by_key("YOUR_SPREADSHEET_ID")
ws = sh.sheet1

# 4) Write all rows in ONE batched call (fast, quota-friendly)
ws.clear()
ws.update("A1", rows)
print("wrote " + str(len(rows) - 1) + " rows to Google Sheets")

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Frequently asked questions

How do I authenticate gspread with Google Sheets?

Create a service account in Google Cloud, enable the Google Sheets API and Google Drive API, and download the JSON key. Then open your spreadsheet and share it with Editor access to the service account email listed in that JSON file. In code, call gspread.service_account(filename="service_account.json") and open the sheet by its key. The key is the long ID in the spreadsheet URL.

Can I scrape directly into Google Sheets without any code?

Yes, if the data is already a public CSV or a plain HTML table. Use =IMPORTDATA("url") for a public CSV or TSV, or =IMPORTHTML("url","table",1) for the first HTML table on a page. These cannot run JavaScript, send headers, or handle browser verification, and a sheet allows only about 50 IMPORT functions. For dynamic pages, use an Apps Script function that calls a scraping API and writes rows with setValues().

Why does IMPORTHTML or Power Query return an empty table?

Because the page builds its table with JavaScript after load, or shows a browser verification interstitial, so the raw HTML those tools fetch contains no rows. Render the page in a real browser first. A scraping API like Scrappey renders the page and routes through residential proxies, then returns finished HTML or autoparsed rows you can write straight into Sheets.

Last updated: 2026-06-08