Tableau and Python / An Introduction

Today, Python is one of the most sought after skills in the world of Data Science, and as such, we can leverage this power in our Tableau Data Visualisations. While integration is not entirely out of the box and requires some initial setup, it is not as hard to get up and running. In this article, we will introduce Python, show you how to integrate Python in Tableau, and more importantly, leave you with an example that you can build on.

Python Programming Language

Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python’s design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python gained a lot of traction in the world of data science and now has a host on fantastic libraries that support your requirements:

Note: this is a short list of popular libraries that can be leveraged in your Python code.

TabPy

TabPy (the Tableau Python Server) is an external service implementation which expands Tableau’s capabilities by allowing users to execute Python scripts and saved functions via Tableau’s table calculations. TabPy allows Tableau to remotely execute Python code.

It has two components:

1. A process built on Tornado, which allows for the remote execution of Python code through a set of REST APIs. The code can either be immediately executed or persisted in the server process and exposed as a REST endpoint, to be called later.
2. A tools library that enables the deployment of such endpoints, based on Python functions.

Tableau can connect to the TabPy server to execute Python code on the fly and display results in Tableau visualizations. Users can control data and parameters being sent to TabPy by interacting with their Tableau worksheets, dashboard or stories.

You can read more about TabPy in the official Github Repository: https://github.com/tableau/TabPy

Getting Started

Note: I will be showing you how to get up and running on Windows, however, the instructions should not be too different if you are an iOS user.

Notice: This section will be updated due to a new version of TabPy

Now that the introductions are out of the way, let us get started by installing Python, TabPy and to get everything integrated.

Download and Install Python

• Go to https://www.python.org and download Python 3.7.3.
• Once downloaded, double click and install Python.
• Now we will add Python to our Path.
  • Click on the Windows Start button and type sysdm.cpl and click enter; this will open your System Properties.
  • Click on the Advanced tab.
  • Click on Environment Variables…
  • Under System variables double click on Path; if Path does not exist, click on New…
  • In Edit environment variable click on New and type the following: C:\Users\YourUserName\AppData\Local\Programs\Python\Python37-32
  • Keep clicking Ok to close all the configuration windows.
• Now we will test that Python has been added to your machine.
  • Click on the Windows start button.
  • Type CMD and click enter to open up Command Prompt.
  • Now type python –version (two hyphens).
  • If you see Python 3.7.3, or whichever version you have installed, Python is working.

Download and Install TabPy

• Go to the following website https://github.com/tableau/TabPy/releases.
• Click on Source code (zip) to download the TabPy code.
• Once the file is downloaded, unzip the contents.
• Go to this directory and double click on startup.cmd.

If all goes well, you should see the following:

Note: if you get a Windows Security Alert, click on Allow access.

Tableau and TabPy

Now we will try to connect Tableau to the TabPy Server.

• Open Tableau Desktop.
• Go to Help in the application menu.
• Go to Settings and Performance and select Manage External Service Connection.
• In the External Service Connection dialogue window:
  • In Select an External Service and select TabPy/External API.
  • In Server type localhost.
  • in Port type 9004, you can see the port that TabPy started on in the image above.
  • Click Test Connection.

If all goes well, you should get a Successfully connected to the external service. and boom, we have installed Python, TabPy and connected to Tableau with TabPy, so are now ready to build a workbook and leverage the power of Python.

Worksheet

We are going to start by opening the Sample Superstore Data Source that is provided with Tableau Desktop. Using this Data Source, we are going to create a Calculated Field to perform Pearson’s Correlation Coefficient (r).

Correlation is a technique for investigating the relationship between two quantitative, continuous variables, for example, age and blood pressure. Pearson’s correlation coefficient (r) is a measure of the strength of the association between the two variables.

Let us create a Calculated Field called Pearson Correlation Coefficient:

SCRIPT_REAL("import numpy as np
return np.corrcoef(_arg1,_arg2)[0,1]",
SUM([Sales]),SUM([Profit]))

Things to note:

• We are importing the NumPy library.
• We are calling the corrcoef function and passing through SUM(Sales) and SUM(Profits).

We will now build our worksheet:

• Change the Mark Type to Circle.
• Drag Category onto Columns.
• Drag Sales onto Columns.
• Drag Profit onto Rows.
• Drag Customer Name onto the Detail Mark.

You should now have the following:

Now we will use our Pearson Correlation Coefficient.

• Drag Pearson Correlation Coefficient onto the Colour Mark.
  • You will see an invalid JSON error, but do not worry about that. Please close the dialogue.
  • Right-click on this object, go to Compute Using and select Customer Name.
• Click on the Color Mark.
  • Click on Edit Colour and select the Red-Green Diverging.

You should now end up with the following:

and boom, we are done. As it stands, we have installed Python, TabPy, Integrated Tableau and Python and created a Calculated Field that sends data to Python and returns a value that is rendered.

Summary

In future articles, I will expand on other possible uses of Python and the value that it can bring to your Tableau experience; I will cover topics like such as Sentiment Analysis, Identifying Outliers, and Data Science techniques to name a few.

I hope you all enjoyed this article as much as I enjoyed writing it. Do let me know if you experienced any issues integrating Tableau and Python, and as always, please leave a comment below or reach out to me on Twitter @Tableau_Magic, till next time, Have fun with Tableau.

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Also, do be sure to check out our various courses:

• Creating Bespoke Data Visualizations (Udemy)
• Introduction to Tableau (Online Instructor-Led)
• Advanced Calculations (Online Instructor-Led)
• Creating Bespoke Data Visualizations (Online Instructor-Led)