Forecasting with Python and Tableau

In this post, I’ll show how I used Python code within Tableau to build an interactive dashboard implementing a time-series forecast. If you just want to play around with the dashboard first and explore the SARIMAX algorithm, download the full python-implemented dashboard here or go to this slightly dumbed-down version on Tableau Public (Tableau Public wisely but disappointingly disallows external scripts to be uploaded, so I had to fake the Python scripts with hard-coded data sets).

TabPy: Because when you can’t think of a good name, try a portmanteau

Last year, Tableau released version 10.2 which included integration with Python. It’s not super straightforward how to use it though, so I thought I’d figure it out when a client asked for a time-series forecast dashboard. ARIMA models are not built into Tableau (Tableau’s Forecast module uses exponential smoothing), and in this particular case I really needed to use the higher predictive capability of the ARIMA algorithm, so TabPy seemed to be my only option.

I won’t go into too much detail on how to install TabPy (hint: pip install tabpy-server) and no detail at all on how to install Python (I figure, if you want to run Python code inside Tableau, you probably already know how to use Python. If not, start here.)

Once you have installed the TabPy distribution you will need to navigate to the source code contained in /site-packages and go subsequently into the tabpy-server directory (in my case, on MacOS with Anaconda 3 installed in the default location, /anaconda3/lib/python3.7/site-packages/tabpy_server). From there run sh startup.sh or python tabpy.py to start up a server. You need to instruct Tableau to constantly sniff port 9004, which is how Tableau and Python communicate. To do this, from within Tableau,

  1. Go to Help → Settings and Performance → Manage External Service Connection…
  2. Enter the Server (localhost if running TabPy on the same computer) and the Port (default is 9004).
And you’re off to the races!

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