11/4/2022 0 Comments Metabase google sheets![]() ![]() Select Other UI for “Where will you be calling the API from?”. Create credentials to use the Google Drive APIĪfter Enabling the Google Drive API, Google should take you to the Google Drive API console for your project. Now we’ve created our Google Drive project. Go to the API & Services dashboard by clicking the Hamburger menu on the left and select API & Services > Dashboard. I had to refresh the page to see my new project. Enable the Google Drive APIĪfter creating your project, Google will take you back to the Developer Console. You can also rename the project ID if you like.Ĭlick Create. Create a Google Developer ProjectĮnter a Project Name - I just use one project for all my notebooks instead of creating a new one for every project so I named my project “Jupyter and Google Sheets”. This part is a bit long and tricky but you only have to do this once for all notebooks and sheets. Part One - Create your Google Developer Credentialsīefore connecting our Jupyter notebooks to our google sheets, we first must create Google developer credentials with permission to access our Google Drive. #METABASE GOOGLE SHEETS FULL#You can see my full enviroment here and my full example notebook used in part 2 here. NOTE: I am using Python 2.7.14 and Anaconda version 4.3.30. #METABASE GOOGLE SHEETS UPDATE#But if you find yourself going back and forth between sheets and Jupyter or occasionally miss the ease of use of spreadsheets, or need to update the data in sheets from Jupyter quickly, then this tutorial is for you. #METABASE GOOGLE SHEETS HOW TO#After tinkering for a bit, I figured out how to easily pull the most up-to-date data from my Google Sheets into Jupyter and output data from Jupyter back to Google sheets. Yuck.īut then I realized that Google provides an API to connect sheets to any third party app you can dream of, including Jupyter notebooks. Rinse and repeat for every debug or new iteration of data. ![]() I used to do this by completing my spreadsheet work in Google Sheets, downloading a CSV file, pulling the CSV file data into Jupyter, manipulating my data, exporting another CSV file, and uploading it back to Google Sheets. Or you may find it easier to do some of your simple data work in a spreadsheet and only the most complex parts in Python. Or you may have to output the fruits of your complex analysis to a spreadsheet so your non-coder CEO or client can read and understand it. You can run complex for-loops to create Monte Carlo simulations without expensive add-ons like Crystal Ball.īut sometimes you need to combine this power with the simple and almost universally understood UI of a spreadsheet.įor instance, you may want to practice your fancy Python code on a small dataset that you can easily and visually manipulate in Google Sheets. You can perform complex statistical operations or otherwise manipulate data in just a few lines of code. ![]() Unlike Google Sheets or Microsoft Excel, they can handle large amounts of data with ease. Jupyter notebooks are incredibly powerful. ![]()
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