I have a published Google Docs document that contains a table with important data. I want to pull this table information using Python code but I’m struggling to find the right approach.
I already tried using the requests library to fetch the document content, but when I parse the HTML response, it’s difficult to locate and extract just the table portion. I also looked into Google’s Documents API, but the authentication setup seems complicated and I keep getting various error messages.
What would be the most straightforward way to programmatically access a table from a Google Docs URL in Python? Are there any specific libraries or techniques that work well for this type of data extraction from Google’s published documents?
Here’s what worked for me: add /export?format=html to the end of your Google Docs URL. You’ll get much cleaner HTML than the regular view. Then just use pd.read_html(requests.get(modified_url).content) - it’ll automatically find and parse all tables into DataFrames. No authentication headaches, and your data’s ready to analyze. Just make sure the doc’s published publicly first or you’ll get permission errors.
Google Docs API authentication looks scary but it’s actually pretty straightforward. Just create a service account in Google Cloud Console, grab the JSON credentials file, and use google-api-python-client. Once you’re authenticated, you can pull document content and tables with documents().get(). The table data returns as structured JSON - way cleaner than parsing HTML. I’ve found this way more reliable than web scraping since Google keeps changing their HTML structure and breaks table parsing. Takes about 15 minutes to set up but you’ll save hours of debugging HTML headaches down the road.