Web Scraping Excel: Import Website Data into Spreadsheets

Excel is still the world’s most common “data pipeline”.

Which is why the keyword “web scraping in Excel” never dies.

The catch: websites aren’t built for spreadsheets. They change, they paginate, they block, and they don’t care that your workbook refresh broke.

This guide gives you a practical decision tree:

• when Excel alone is enough
• how to handle common failure modes (pagination, JS-heavy pages)
• when to switch to a tiny Python helper (and where ProxiesAPI fits when you scale)

Option 1: Power Query (best default for simple HTML tables)

Power Query is the cleanest native approach when:

• the page is server-rendered HTML
• the data is in an actual table/list
• you don’t need heavy navigation (logins, infinite scroll)

Workflow:

1. Data → Get Data → From Web
2. paste the URL
3. select the table(s) Power Query detects
4. transform/clean in the query editor
5. load to a sheet or the data model

When Power Query breaks

Power Query fails when:

• the data is JS-rendered
• the site rate limits refreshes
• the “table” is actually div soup
• pagination requires interacting with the UI

If your query is brittle, don’t keep “fixing the workbook”. Move the scraping upstream.

Option 2: Excel formulas (good for tiny, stable pulls)

Formula-based scraping is fragile, but it’s fine when:

• you’re pulling a single value (a number, a title)
• the HTML is stable
• it’s not mission-critical

If you need more than a handful of fields, formulas become unmaintainable quickly.

Pagination: the biggest “Excel scraping” trap

Most real datasets span multiple pages:

• product lists
• blog indexes
• directories

Power Query can handle pagination if:

• the page number is in the URL (?page=2)
• or there’s a predictable “next” link to follow

But infinite scroll and JS pagination usually need a scripted scraper.

Practical rule: if you can’t express “next page” as a URL pattern in under 5 minutes, don’t fight Excel. Use Python.

Option 3: A tiny Python helper (reliable + schedulable)

The most robust pattern is:

1. scrape with Python
2. write CSV
3. Excel imports the CSV (and refreshes it)

This moves the brittle part (fetching/parsing) out of Excel and into code you can test.

Here’s a minimal scraper you can schedule and then refresh in Excel:

from __future__ import annotations

import csv
import os

import requests
from bs4 import BeautifulSoup

URL = "https://example.com/directory"
OUT = "export.csv"
TIMEOUT = (10, 30)

HEADERS = {
    "User-Agent": "Mozilla/5.0 (compatible; ProxiesAPI-Guides/1.0; +https://proxiesapi.com)",
    "Accept-Language": "en-US,en;q=0.9",
}


def build_proxies() -> dict[str, str] | None:
    proxy = os.getenv("PROXIESAPI_PROXY")
    if not proxy:
        return None
    return {"http": f"http://{proxy}", "https": f"http://{proxy}"}


PROXIES = build_proxies()


def fetch(url: str) -> str:
    r = requests.get(url, headers=HEADERS, timeout=TIMEOUT, proxies=PROXIES)
    r.raise_for_status()
    return r.text


def parse(html: str) -> list[dict]:
    soup = BeautifulSoup(html, "lxml")
    rows = []
    for card in soup.select(".card"):
        name = card.select_one(".name")
        price = card.select_one(".price")
        rows.append(
            {
                "name": name.get_text(" ", strip=True) if name else None,
                "price": price.get_text(" ", strip=True) if price else None,
            }
        )
    return rows


if __name__ == "__main__":
    html = fetch(URL)
    rows = parse(html)
    with open(OUT, "w", newline="", encoding="utf-8") as f:
        w = csv.DictWriter(f, fieldnames=["name", "price"])
        w.writeheader()
        w.writerows(rows)
    print("wrote", OUT, "rows:", len(rows))

ProxiesAPI integration point

If your scheduled fetch starts failing (rate limits, intermittent blocks), set:

NaN NaN NaN

NaN

NaN 0 NaN 0 NaN NaN NaN NaN NaN NaN 0 NaN