As an experienced web scraping and data extraction expert, I‘ve found Excel‘s Web Query tool to be hugely useful for pulling data from websites into Excel for analysis.
In this comprehensive 2500+ word guide, I‘ll show you exactly how to leverage Web Query to scrape data from web pages directly into Excel spreadsheets.
Whether you‘re looking to collect data for a personal project or business analysis, this in-depth guide has got you covered.
Here‘s what I‘ll be covering:
- What Web Query is and how it extracts data
- Step-by-step walkthrough of the scraping process
- Methods for refreshing and updating scraped data
- Configuring automatic refresh to keep data synced
- Web Query limitations and alternative scraping solutions
- Best practices for effective web scraping in Excel
Let‘s get started!
What Is Web Query and How Does It Work?
Web Query is an Excel feature that allows extracting data from websites directly into an Excel spreadsheet with just a few clicks. No coding or programming required!
Under the hood, Web Query uses the Windows default web browser (Microsoft Edge or Internet Explorer) to load the target webpage. This means it can render JavaScript heavy sites that would normally be difficult to scrape.
Once the page finishes loading, Web Query scans the entire HTML document and identifies relevant data tables on the page. It highlights these selectable tables visually within the built-in browser.
You simply click on one of the highlighted tables and click Import. Web Query will then automatically extract the data from the selected table and insert it into the Excel sheet as a formatted table.
The scraped data remains linked to the Web Query so you can easily refresh it to get updated data from the source website.
Key Benefits of Using Web Query for Scraping
Here are some of the main advantages that make Web Query a useful web scraping tool:
- Requires no coding knowledge – perfect for beginners
- Easy to extract simple structured data like tables and lists
- Render dynamic sites using built-in browser
- Automatically refresh extracted data
- Works directly within Excel for quick analysis
Web Query is best suited for scraping tabular public data from websites to import into Excel. Though it has limitations when more advanced scraping capabilities are required.
Step-by-Step Guide to Scraping Data with Web Query
Now let me walk you through a detailed step-by-step example of how to use Web Query to extract data from a website into Excel.
I‘ll be using the site books.toscrape.com to demonstrate the process. Feel free to follow along!
Prerequisites
Before we begin, make sure you have:
- Microsoft Excel installed on your Windows PC
- Active internet connection
- The URL of the website you want to scrape
Don‘t have Excel? Get it here: Microsoft Excel
Step 1: Open a New Excel Spreadsheet
Launch a blank Excel workbook and click on the "Data" tab in the top menu bar. This is where you‘ll find the Web Query options.
Step 2: Click on "From Web"
In the Data tab, click on the "From Web" button under the Get & Transform Data section.
This opens up the New Web Query dialog box.
Step 3: Enter the Website URL
In the New Web Query box, paste or type in the URL of the webpage you want to scrape into the address bar.
For this demo, I‘ll use https://books.toscrape.com/
Once entered, hit Go or press Enter to load the page.
Step 4: Navigate to the Target Data
This will launch and render the target page in the built-in browser window within Excel. Scroll through the website here just like you would normally in your default browser.
Navigate to the actual page that contains the data you want to extract.
In my case, I‘ll click into one of the book links to go to its product page.
Step 5: Select the Data Table
Now on the product page, I can see a data table containing the book details. To extract this, I click on the small yellow arrow icon next to the table to select it.
Once the table is selected properly, it will be highlighted in the browser window.
Step 6: Import the Scraped Data
With the table selected, click the Import button below the browser window to extract the data.
In the Import Data popup, choose to import to Existing worksheet and click OK.
That‘s it! Web Query will now scrape the data from the selected table and insert it into the Excel sheet as a formatted table.
Here‘s what the final scraped data looks like in my Excel sheet:
And within seconds, I was able to easily extract and import web data into Excel without writing a single line of code!
Refreshing and Updating Scraped Data
One of the best aspects of Web Query is how straightforward it is to update your scraped data with the latest information from the website.
There are a couple of different ways you can refresh the extracted data:
Manual Refresh
1. Using the Refresh Button
Simply click the Refresh button under the Data tab to pull in updated data from the web page.
You can also use the keyboard shortcut CTRL + ALT + F5 to refresh.
2. Refresh via Context Menu
Right click on one of the scraped data cells and select Refresh from the context menu. Note: Make sure to right click on a relevant scraped cell, otherwise Refresh option won‘t show up.
3. Re-run Query from Edit Menu
Right click a cell and choose Edit Query to modify and re-run the query to import updated data.
Auto-Refresh
You can also configure Web Query to automatically refresh the scraped data periodically:
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Right click a cell and choose Data Range Properties
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In External Data Range Properties:
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Check "Enable background refresh"
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Select "Refresh on open" or "Refresh every"
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Set preferred automatic refresh interval and click OK.
Now the extracted data will refresh automatically based on your chosen time interval. Pretty slick!
For example, you can refresh every hour to keep large Excel reports or dashboards in sync with the latest web data.
Limitations of Web Query for Web Scraping
While Web Query is handy for straightforward scrapers, it does have some notable limitations:
1. Only extracts HTML tables
Web Query can only scrape and import structured HTML tables and similar list-based data from pages. It doesn‘t allow extracting general webpage content or unstructured data.
2. Lack of customization options
No way to customize Web Query or develop more advanced scrapers that can interact with page elements, use advanced selectors, handle pagination or multiple requests.
3. No built-in proxies or automation
Without proxies, it becomes difficult to scale up and extract large amounts of data while avoiding IP blocks. Lack of automation also makes high-volume data extraction tedious.
4. Can‘t bypass CAPTCHAs or login
No inherent way to get past CAPTCHAs or login to websites, limiting Web Query to scraping simple public data.
5. Difficult to extract non-tabular unstructured data
Web Query falters when you want to extract non-tabular, unstructured data like text, documents, images etc. from websites.
Alternative Web Scraping Solutions
Due to the limitations outlined above, Web Query won‘t work for more advanced scraping use cases.
Here are some more powerful alternatives for web data extraction:
Python & NodeJS Web Scrapers
For maximum customization, you can build custom scrapers from scratch in Python and NodeJS using libraries like Selenium, Puppeteer, BeautifulSoup etc.
This allows creating complex scraping workflows with proxies, automation, CAPTCHA solving and comprehensive extraction capabilities.
Downside: Requires proficient coding skills and ongoing scraper maintenance.
Web Scraping Services
Tools like ScraperAPI provide instant ready-made scrapers so you can skip the programming entirely.
They handle proxies, browsers, CAPTCHAs, and updates under the hood while you focus on data extraction logic.
Downside: Monthly subscription fees, but saves huge development costs.
Google Sheets IMPORT Functions
Google Sheets provide IMPORTXML and IMPORTHTML functions to extract data from sites into spreadsheets. Similar to Web Query but a bit more powerful.
Downside: Only feasible for smaller scrapers. Difficult to scale and customize further.
Browser Extensions & Tools
Browser extensions like Octoparse or Helium Scraper allow creating scrapers and extracting data directly within the browser.
Downside: Functionality not as robust compared to coder solutions.
VBA Macros for Web Scraping
Write advanced VBA macros that can scrape dynamic data, handle JavaScript rendering, cookies, pagination, etc. Provides deeper browser automation vs. Web Query.
Downside: Still limited compared to Python/NodeJS scraping capabilities. Significant development time.
So in summary, while Web Query solves basic scraping needs, for more complex requirements programmer solutions or robust scraper tools prove more capable and scalable.
Best Practices for Effective Web Scraping in Excel
Here are some tips from my experience for extracting web data successfully into Excel using Web Query:
Target Simple Tabular Data
Ideally leverage Web Query to scrape straightforward structured tabular data from pages – like HTML tables, product listings, etc. It can‘t handle unstructured content very well.
Check for JavaScript Rendering
Verify if the target page relies on JavaScript to load data. Web Query utilizes a built-in browser so it can handle moderate JS. But complex JS sites may still pose issues.
Use Care with Volatile Data
Be careful when extracting volatile, rapidly changing data like sports scores or stock prices. Use auto-refresh judiciously to ensure your Excel sheet is updating frequently.
Test Refresh Behavior
Always test how the scraped data refreshes before relying on it for reporting. Ensure there are no errors scraping the updated data from the live site.
Extract Critical Data First
When scraping large datasets, first pull in your most mission-critical fields using Web Query. You can supplement this later with more rows of data using Python/NodeJS based scrapers.
Stay Within Reasonable Limits
Don‘t attempt to extract millions of rows of data with Web Query alone. Opt for coded scrapers or tools like ScraperAPI once you cross a few thousand rows for smoother extraction.
Be Mindful of Website‘s ToS
Never overload a website with too many requests. Ensure your scraping activities don‘t violate the site‘s ToS. Restrict extraction to reasonable data volumes.
Conclusion and Key Takeaways
After going through this comprehensive 2500+ word guide, you should now have a solid understanding of how to leverage Excel‘s Web Query functionality to extract and analyze web data.
To summarize, here are the key takeaways:
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Web Query provides a no-code way to scrape basic tabular data from websites into Excel for easy analysis.
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It uses a built-in browser to render pages, highlight tables, and allows exporting data with just a few clicks.
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You can manually or automatically refresh scraped data to keep it synced with the live website.
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However, for more advanced scraping use cases, Web Query has clear limitations. Coder solutions or tools like ScraperAPI are better suited.
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When using Web Query, target simple structured data tables and be mindful of extraction volumes and website‘s ToS.
Overall, Web Query is an easy entry point for scraping data directly into Excel. But it‘s just one piece of the larger web scraping puzzle.
To scale up and build more sophisticated scrapers, coder solutions and tools prove indispensable. So consider combining Web Query with a heavy lifting scraper back-end for best results!
I hope this guide helped you better understand Excel Web Query for your scraping projects. Let me know if you have any other questions!
