I’m just starting out with Python programming and need some guidance. I’ve already gone through the process of setting up Google API access for my account.
What I’m trying to do
I have a CSV file that’s currently saved in my Google Drive folder. What I want to accomplish is to load this file directly into a pandas DataFrame using Python code, without having to download it manually first.
My question
Is there a way to programmatically access and read CSV files from Google Drive and convert them into pandas DataFrames? I’m looking for a solution that works within Python and takes advantage of the Google API setup I already have.
Any code examples or step-by-step guidance would be really helpful since I’m still learning the ropes with both pandas and Google Drive integration.
yeah, there’s an easier way - make ur CSV public in google drive and grab the file ID from the URL. then use pd.read_csv('https://drive.google.com/uc?id=YOUR_FILE_ID') directly. skip the api stuff unless u need private access.
To load a CSV file from Google Drive into a pandas DataFrame, start by ensuring that you have authenticated your Google Drive API access correctly. Next, obtain the file ID from the URL of your CSV file in Google Drive. Using the Google Drive API, you can access the file and utilize its export capability to retrieve the CSV data. You can then use pd.read_csv() alongside StringIO to convert the data into a DataFrame seamlessly. This method does not require manual downloading, making it efficient for your needs.
Since you’ve got Google API access set up, just use PyDrive - it makes authentication way easier. Install it with pip install PyDrive, authenticate once, then use the file ID to pull CSV content straight into memory. PyDrive handles OAuth much cleaner than raw API calls. Once you’ve got the file content, feed it directly to pandas with pd.read_csv(StringIO(file_content)). I’ve used this setup for multiple CSVs in shared drives and it keeps your auth session alive between script runs.