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As an optional requirement, if the number or data type of columns in your function’s output is different to the input from Prep, then you will need to create a function called get_output_schema to tell Prep what schema to expect from the script.

Note that in some cases you might be using a script purely to fetch external data as an input to Prep, in which case you can simply send it a dummy value.

One or more functions to be executed by Prep that accept a dataframe as their only input and return a result, which Prep will take as the script step’s output.

So what does your python script need to contain?

You can run it manually, use Prep Conductor in Tableau Server with the data management add-on, or from the command line as of now Tableau Online doesn’t support external service connections.

Insert one or many Script steps into your flow, point them at your pre-written script file, and tell them which function to use.

By default TabPy runs on port 9004 here’s how the console looks when it’s running: How exactly you do this depends on your installation, I do this through Anaconda Prompt using the tabpy command.

Develop your script(s) I personally use Spyder (which comes with Anaconda) but there are loads of alternatives including P圜harm, Jupyter, and SublimeText.

#Tableau prep 2020.1 install

Install TabPy using pip – simply a case of typing pip install tabpy in your command line.

Update, 25th March 2020: As of Tableau 2020.1, TabPy is now fully supported by Tableau and has an easier installation process, as well as some pre-built statistical functions for easy use in Tableau (or anywhere else!) and other enhancements. Integrating Tableau with Python requires TabPy, a framework that allows Tableau to execute Python code and pass data to it. The Script step in Prep allows you to connect to a Python or R service running on a machine (either the same computer that’s running Prep or a separate server) and pass the data in Prep to it as a dataframe for R this would be a native dataframe and for Python this means a pandas dataframe.

#Tableau prep 2020.1 for mac

This post offers a decent summary of where to start and the differences between Python 2.7 and 3.x (even for mac users, although macs come with Python 2.7 installed), as does this article from Real Python. If you haven’t, the Python downloads page here but beware that choosing which version(s) to install can be frankly confusing (not to mention whether to use the Anaconda distribution or not). The rest of this post assumes that you have already installed python on your machine. Here’s a step-by-step guide!Īll the files for this blog post can be downloaded from here.

Tableau can already integrate with Python as a table calc on aggregated data, but Prep integration allows us to run Python functions at row level which is more suitable for use cases like sentiment analysis, where it’s most helpful to score text row-by-row before aggregating in a viz. With the release of the Script step in Tableau Prep 2019.3, it’s possible to integrate Python with Tableau Prep to fetch data from web APIs, use Google’s Geocoding API to fetch postcodes, or deploy predictive models on your data, to give just a few examples. Now we have the percentile that is fixed in our data, so it’s easy to filter and aggregate without disrupting the percentile calculation.įor the moment, these are the top use cases that have come to my mind but I’m sure there are more out there – please let me know of any in the comments below or tweet me ! Note that you can’t do calculations inline – and if you try you’ll get this error message: As of version 2020.1.3, Tableau Prep now has the capability to create analytic calculations and