TabPy v2.2.0 Released

TabPy version 2.2.0 is released:

To install or update to the latest version as usual run

pip install --upgrade tabpy

The release includes fixes for authentication:

  • Fixed bug for scripts with tabpy.query(...) calls for when authentication is configured for TabPy.
  • Fixed bug for TabPy reporting 500 error instead of 401 when it runs without the attached console.
  • Improved authentication security (this is breaking change) – now TabPy returns authentication error when credentials are provided, but it is not configured for it.

Additional reads:

How to use Python modules for TabPy scripts in Tableau

TabPy supports deployed functions which are recommended way for reusing Python code, creating and sharing models and moving code form SCRIPT_... calculated fields outside of workbooks. However when working or experimenting with code it is not very convenient if you’ll have to deploy new version of a function with every change. And this is why you may have a question if it is possible just to invoke some code from standalone Python file.

And it is possible! Let me show you how.

The files with Python function(s) in them should be on the same machine where your instance of TabPy is running. Let’s create simple file with couple of simple functions in it:

def my_add_lists(list1, list2):
    return [x + y for x, y in zip(list1, list2)]

def my_inc_list_items(list1):
    return [x + 1 for x in list1]

For Python to be able to find the module ( file) we need to set up environment variable PYTHONPATH. As explained in Python documentation ( PYTHONPATH augments where Python is looking for modules when they are referenced with import. The variable needs to be set before TabPy is started.

For Windows the variable can be set in command line:

set PYTHONPATH=%PYTHONPATH%;<my-python-functions-folder>

where <my-python-functions-folder> is the path to where your Python module(s) are located, e.g.:

set PYTHONPATH=%PYTHONPATH%;c:\user\ogolovatyi\python\tabpy-experiments

For Linux and Mac the variable can be set similarly:

export PYTHONPATH=$PYTHONPATH:<my-python-functions-folder>

Now in calculated fields you can import the modules and use functions from them. For example:

from my_python_functions import my_add_lists

return my_add_lists(_arg1, _arg2)
SUM([Price]), SUM([Tax])

In the example above from my_python_functions import my_add_lists tells Python to load my_python_functions module (which will be file in the folder we previously added to PYTHONPATH) and load my_add_lists function from it.

After that the function is used in the calculation.

Hope this simple example is helpful for you when working on Python code to be used in Tableau calculations.

TabPy v1.1.0 released

TabPy version 1.1.0 is released: package –, release on GitHub –

You can update your TabPy with the following command (you’ll need to stop all running TabPy instances first):

pip install --upgrade tabpy

New release main improvement is for /info method ( – now it checks for credentials to be provided if TabPy is configured for authentication. The improvement won’t affect any older Tableau Desktop or Tableau Server versions which already had support for TabPy authentication.

For how to configure authentication for TabPy read How to configure TabPy with authentication and use it in Tableau.

How to run TabPy with Anaconda on Linux

What Anaconda is can be explained in many ways depending on how and what you use is for. In the case of TabPy Anaconda serves as Python environments manager which allows having different Python environments (different versions of Python with a different set of packages) on the same machine. To learn more about Anaconda and where it can be used to start at

First question to ask is why you may need to use Python environments. There are many reasons. Couple of the most important are:

  • Your OS may have old version of “system wide” Python (e.g. Python 2.7) and it is not easy (or not possible at all) to update it without breaking something, and
  • You may want to isolate TabPy environment with packages it needs from your other environment (development, training models, etc.), or
  • You may want to have multiple independent TabPy instances each with their own set of packages awailable.

To manage Python environments Anaconda is not the only option, you can use Python virtualenv tools for example – However Anaconda has some advantages like nice UI app to manages environments and packages, saving/restoring/sharing enviroments, support for R environments, etc.

And when for Windows and Mac you can use UI app in many cases for Linux GUI is not an option. In other words you will need to use command line. And this is what rest of this post shows how to do.

After login to a Linux machine, you need to download Anaconda installer from The following commands assume you are in your home folder (run cd ~ to navigate to your home folder). On the Anaconda distribution page find the link for the latest version for Linux and run the following command:


Now when you have the installer in your home folder run it:


When the installer finishes add path to the Anaconda to the environment variable (this assumes bash is your Linux shell):

echo 'export PATH="~/anaconda3/bin:$PATH"' >> ~/.bashrc

In the command above we add anaconda3 subfolder bin from the user home folder to environment variable PATH so anaconda commands can be found regardless of what is the current folder. If you installed Anaconda in a different location the command need to be modified properly.

Now let’s restart shell (or you can unlog and log in again):

source ~/.bashrc

Next let’s update Anaconda:

conda update conda

In the command above conda is the tool which will let us perform different Anaconda operations.

Next step is to create new environment for TabPy:

conda create --name TabPy python=3.7

What happens in the command above is new environment with name TabPy is created and it has Python 3.7 available for it.

You can have as many environments as you need with different or the same Python versions. As all of them are isolated and do not interfere it is possible to have different sets of packages or the same packages of different versions in them.

Now we can activate the environment:

conda activate TabPy

If the command succeeds you’ll the command prompt is updated to have the environment name in it.

And now you can install TabPy and other packages:

python -m pip install --upgrade
pip install tabpy

To exit from the environment run deactivate command:

conda deactivate

For how to configure TabPy read

Add colors to TabPy console output

Let’s brighten the day, shall we?

In one of my previous posts, I explained how logging can be configured for TabPy: levels, the format of the message, where it is sent/stored, etc. Read here for more detail – How to Configure Logging in TabPy?

I also mentioned there is number of handlers from provided with Python (console, file, and others). But it is also possible to use third party or customized handlers. With this post, I am going to show how you can make console logging colorful with third-party formatter.

First, we need a formatter. You can choose any, I for the demonstration purposes picked up colorlog ( The most important thing you need to pay attention to is how the formatter messages are formatted… Yes, you’ll need to define the format for the formatter. For the one I chose documentation for the format arguments is on the PyPi and GitHub ( pages for the package.

Let’s install the package:

pip install colorlog

Next, we need a configuration file (additional reading – TabPy: modifying default configuration):






format=%(asctime)s [%(bold)s%(log_color)s%(levelname)-8s%(reset)s] %(log_color)s%(message)s%(reset)s
datefmt=%y/%m/%d %H:%M:%S

In the file everything till line 20 is standard: define loggers, handlers, and formatters. And now with the only formatter defined we want to use formatter from the package previously installed. The line class=colorlog.ColoredFormatter tells Python logger to use specified formatter (ColoredFormatter) from colorlog package.

For the format parameter which defines what will be in a logged message we use formatter specific arguments: %(bold) makes text bold, %(log_color) sets the color for following text, and %(reset) changes text attributes to default. Again – for any other formatter you choose to use the arguments list most likely will be different.

Running TabPy with the config and querying it will look something like this: