I have a published Google document with a table inside it and I want to pull out all the table information using Python. The document is publicly available through a sharing link but I can’t figure out the right way to grab the table content programmatically.
I tried using basic HTTP requests to fetch the page content but that didn’t work well for parsing the structured data. I also looked at using Google’s document API but I keep running into authentication issues and I’m not sure if that’s the right approach for a public document.
What’s the best method to read table data from a Google Docs file when you have the public URL? Are there any Python libraries that make this easier?
I ran into the same issue last month and found that converting the Google Doc to HTML format works better than trying to parse the regular document view. You can modify your sharing URL by replacing ‘/edit’ or ‘/view’ with ‘/export?format=html’ at the end. This gives you clean HTML that’s much easier to work with using BeautifulSoup.
Once you have the HTML version, you can use requests to fetch it and then parse the table tags directly. The table structure in the exported HTML is pretty standard so finding rows and cells becomes straightforward. I was able to extract my data in about 15 lines of code this way, compared to the authentication headaches I had with the official API approach.
honestly selenium might be overkill but it works if other methods fail. just use webdriver to load the public doc url and grab table elements with find_elements. bit slower than api calls but doesnt need auth setup and handles js-rendered content that requests library might miss.
Another approach that worked for me is using the Google Docs API without authentication for public documents. You can extract the document ID from your sharing URL and make a direct API call to https://docs.googleapis.com/v1/documents/{DOCUMENT_ID} without needing API keys since the document is public. The response gives you structured JSON data that includes all table content with proper formatting preserved. I found this method more reliable than HTML parsing because it maintains the original table structure and cell relationships. The JSON response has a clear hierarchy where tables are nested under document elements, making it easier to iterate through rows and extract specific data. Just make sure your document sharing settings allow public access without sign-in requirements.