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Overview

If you’re doing an analysis for a stakeholder that lives in spreadsheets (FP&A, we’re looking at you), when you share a Smart Report with them, there’s a good chance that the first thing they do is export the data.

We want you to be able to meet your stakeholders where they are. With the push-to-Google Sheets functionality, you can do your entire analysis within Fabi.ai (or even build complex prediction models) and push that data back to a Google Sheets in just a few clicks.

This is also useful functionality if you or your team need to provide regular executive reports in Google Slides and don’t want to deal with the tedious task of copy/pasting data on a weekly basis.

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Preparing the DataFrame

Start by creating your Python DataFrame. You can simply write a SQL query using a SQL cell, which will automatically generate a DataFrame as shown in the following screenshot with dataframe1:

Or you can create a DataFrame in Python as shown here:

In the example above, we’re using a machine learning model to cluster NBA players by certain attributes in order to group them (hint, this feature is great for building custom lead scoring or churn prediction models or A/B test analysis and sharing with non-technical stakeholders).

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Configuring Google Sheets

Once you’ve created your DataFrame, add a new Google Sheets Push cell to your Smartbook or workflow.

In the cell, select the DataFrame you want to push to Google Sheets and configure the following:

• Sheet URL: The URL of the Google Sheet you want to push data to
• Worksheet Name: The name of the sheet you’re pushing the data to
• Push Mode: Whether you want to override the existing spreadsheet on every update or create a new sheet

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Scheduling updates

Once you’ve created your DataFrame and configured the Google Sheet, don’t forget to schedule and publish the Smart Report, which will determine the refresh frequency of your spreadsheet. For more information on how to publish and schedule reports, see our documention on Building Smart Reports.