Schlagwort: Advanced Analytics

TabPy Tutorial: How to Integrate Python with Tableau for Advanced Analytics

Python in Tableau: TabPy allows Tableau to execute Python code on the fly
Python in Tableau: TabPy allows Tableau to execute Python code on the fly

In 2013, Tableau introduced R Integration, the ability to call R scripts in calculated fields. This opened up possibilities such as K-means clustering, Random Forest models, and sentiment analysis. With the release of Tableau 10.2, we can enjoy a new, fancy addition to this feature: the Python Integration through TabPy, the Tableau Python Server.

Python in Tableau: The Perfect Blend

Python is a widely used general-purpose programming language, popular among academia and industry alike. It provides a wide variety of statistical and machine-learning techniques and is highly extensible. Together, Python and Tableau are the data science dream team to cover any organization’s data analysis needs.

In this tutorial, I’m going to walk you through the installation and connecting Tableau with TabPy. I will also give you an example of calling a Python function from Tableau to calculate correlation coefficients for a trellis chart.

Step by Step: Integrating Python in Tableau

1. Install and start Python and TabPy

Start by clicking on the Clone or download button in the upper right corner of the TabPy repository page, downloading the zip file, and extracting it.

TabPy download via GitHub web page

Protip: If you are familiar with Git, you can download TabPy directly from the repository:

> git clone git://github.com/tableau/TabPy

TabPy download via Git command line interface

Within the TabPy directory, execute setup.sh (or setup.bat if you are on Windows). This script downloads and installs Python, TabPy, and all necessary dependencies. After completion, TabPy is starting up and listens on port 9004.

2. Connecting Tableau to TabPy

In Tableau 10.2 (and later versions), a connection to TabPy can be added in Help > Settings and Performance > Manage External Service Connection:

Tableau main menu
Tableau main menu

Set port to 9004:

External Service Connection dialogue
External Service Connection dialogue

3. Adding Python code to a Calculated Field

You can invoke Calculated Field functions called SCRIPT_STR, SCRIPT_REAL, SCRIPT_BOOL, and SCRIPT_INT to embed your Python script in Tableau:

SCRIPT_REAL('
import numpy as np
return np.corrcoef(_arg1,_arg2)[0,1]
',
SUM([Sales]), SUM([Profit]))
view raw corrcoef_python_tableau.txt hosted with ❤ by GitHub
Python script within Tableau
Python script within Tableau

4. Use Calculated Field in Tableau

Now you can use your Python calculation as Calculated Field in your Tableau worksheet:

Tableau workbook featuring a Python calculation
Tableau workbook featuring a Python calculation

Feel free to download the Tableau Packaged Workbook (twbx) here.

Further Reading: Visual Analytics with Tableau

Join the data science conversation and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Python in Tableau and other data analytics topics. If you’re looking to master Tableau, don’t forget to preorder your copy of my upcoming book, Visual Analytics with Tableau (Amazon). It offers an in-depth exploration of data visualization techniques and best practices.

Also, feel free to comment and share my TabPy Tutorial tweet:

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Data Science Toolbox: How to use R with Tableau

Recently, Tableau released an exciting feature that enhances the capabilities of data analytics: R integration via RServe. By bringing together Tableau and R, data scientists and analysts can now enjoy a more comprehensive and powerful data science toolbox. Whether you’re an experienced data scientist or just starting your journey in data analytics, this tutorial will guide you through the process of integrating R with Tableau.

Step by Step: Integrating R in Tableau

1. Install and start R and RServe

You can download base R from r-project.org. Next, invoke R from the terminal to install and run the RServe package:

> install.packages("Rserve")
> library(Rserve)
> Rserve()

To ensure RServe is running, you can try Telnet to connect to it:

Telnet

Protip: If you prefer an IDE for R, I can highly recommend you to install RStudio.

2. Connecting Tableau to RServe

Now let’s open Tableau and set up the connection:

Tableau 10 Help menu
Tableau 10 External Service Connection

3. Adding R code to a Calculated Field

You can invoke R scripts in Tableau’s Calculated Fields, such as k-means clustering controlled by an interactive parameter slider:

SCRIPT_INT('
kmeans(data.frame(.arg1,.arg2,.arg3),' + STR([Cluster Amount]) + ')$cluster;
',
SUM([Sales]), SUM([Profit]), SUM([Quantity]))
view raw kmeans_r_tableau.txt hosted with ❤ by GitHub
Calculated Field in Tableau 10

4. Use Calculated Field in Tableau

You can now use your R calculation as an alternate Calculated Field in your Tableau worksheet:

Tableau 10 showing k-means clustering

Feel free to download the Tableau Packaged Workbook (twbx) here.

Connect and Stay Updated

Stay on top of the latest in data science and analytics by following me on Twitter and LinkedIn. I frequently share tips, tricks, and insights into the world of data analytics, machine learning, and beyond. Join the conversation, and let’s explore the possibilities together!

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