Using TabPy to Blend Python with Tableau: 4 Easy Steps

• February 2nd, 2022

TabPy | Hevo Data

Data and Visualization are becoming an essential aspect of today’s digital world. Businesses often use data to interpret the current market scenario and make decisions based on that. Visualization helps companies analyze data in charts and graphs and allows them to make data-driven decisions. This blog post will discuss Tableau and connect via Python to perform various business transformations.

In this article, you will be provided with an introduction to Tableau, Python, and the procedure to integrate Python scripts with Tableau using TabPy.

Table of Contents

What is Tableau?

TabPy: Tableau Logo | Hevo Data

Tableau is one of the leading visualization tools available in the market, with several in-house connectors that help users connect to various data sources. Users can create visual masterpieces with a perfect blend of graphical elements like charts, tables, colors, and labels to help the business make a market or data-driven decisions with a vast collection of functions.

Tableau is available as different products, and users can use it based on their requirements. The various product offerings are:

  1. Tableau Desktop
  2. Tableau Reader
  3. Tableau Public
  4. Tableau Online
  5. Tableau Server

Key Features of Tableau

  1. Tableau provides extensive features in Dashboard to perform Advanced Analytics on the data and allows users to create a visual masterpiece. 
  2. With Tableau, users can collaborate sheets among colleagues and other team members to review the designs.
  3. Tableau supports real-time data as well as batch data with robust in-memory computation.
  4. Tableau has over 200+ connectors available in its library, which can connect to any relational and non-relational databases, CSV files, Excel, Hive, Snowflake, etc.
  5. Tableau allows users to develop advanced charts and graphs to create high-quality visuals.

What is Python?

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Python is a high-level, interpreted, and dynamically typed programming language. It has a built-in data structure that allows users to dynamically type the code, thereby making it very attractive for Rapid Application Development. Python is platform-independent and can be used without change in major platforms. 

Python has vast libraries to connect various tools, and hence it is becoming popular in automation and data science projects. One such module that we will be discussing here is TabPy which can be used to connect Python with Tableau. 

Key Features of Python

  1. Python is easy to type and is very similar to typing in the English language with some mathematical semantics. 
  2. Python doesn’t use braces to club the statement; instead, it uses indentation to define scope, and club statements for loops, functions, and classes. 
  3. Python uses newlines to complete a command instead of other programming languages, which often use semicolons or parentheses.
  4. Python is platform-independent, which means code written on windows can run on a Unix machine without any effect.
  5. Python allows developers to write code in fewer lines as compared to other programming languages.

With the help of the TabPy module, Python can easily be connected to Tableau as an external service. It allows you to perform actions like row numbers, ranking, cleaning operations, and many more. 


  1. Download and install Python version 3.6 or later.
  2. Download and install the Tableau Python server
  3. Install Pandas. Pandas data frame can be used to integrate with Tableau Prep Builder.

What is TabPy?

TabPy, an Analytics Extension from Tableau, allows users to execute Python scripts and saved functions using Tableau. With the integration of Python in Tableau, you can incorporate your Python Scripts and use Tableau to visualize the results from your analysis. TabPy utilizes a large number of Machine Learning libraries and allows you to perform customized analyses using Python.

You can further control the data you send to TabPy by working with Tableau Worksheet, or Dashboard. With this integration, you get the ability to customize dashboards, and also leverage the Analytics Capability of Tableau to get a better understanding of the project you’re working on.

Data Preparation is an important aspect of Data Analysis, and Data Scientists tend to spend a lot of their time doing that. This integration automates the Data Exploration and Data Visualization part for the developers to focus more on the Data Science logic.

Installing and Running TabPy

To use Python in Tableau, you need to install TabPy. You can download TabPy from the GitHub Repository Once the download is finished, run the command line to install TabPy by using pip:

pip install tabpy

Once the installation is successful, you can run TabPy using the following command:


Now, you need to go to Tableau to configure the connection. Go to the “Help” menu from the navigation bar and choose “Settings and Performance“. Select “Manage Analytics Extension Connection“.

In the pop-up window, set up the Server and Port. Select “TabPy/External API” as the Analytics Extension. By default, TabPy will be running in your localhost on port 9004. Once done, click on “Test Connection“, and Tableau will display a “Successfully connected to the analytics extension.” message.

How to Use TabPy?

After Tableau is linked to TabPy, you can now execute different operations on your dataset using Python Script. To do so, you need to do the following:

  • Import/load your data set into a new Worksheet in Tableau.
  • Create a Calculated Field in Tableau.
  • Insert the Python Script into the Calculated Field.

Passing Expressions to PythonThe expressions in Tableau must be passed through any one of the following four functions: SCRIPT_BOOL, SCRIPT_REAL, SCRIPT_INT, and SCRIPT_STR, to go to Python.

Procedure to Integrate Python Scripts with Tableau

To integrate Python scripts in the flow, you need to create a connection between Tableau and TabPy server. Once the connection is established, you can use Python scripts to apply functions to the data using pandas data frame. 

You have to specify connection details to securely pass the data to the TabPy server, apply the transformation, and return the data in the data frame format.

Part 1: Connect Tableau Python Server & Tableau Server

Connecting Tableau Python Server and Tableau server is a straightforward process. Use the below instructions to set up the connection. 

Step 1: Launch the shell to execute the below commands and enter the below commands:

tsm security maestro-tabpy-ssl enable --connection-type {maestro-tabpy/maestro-tabpy-secure} --tabpy-host <IP address or host name for TabPy> --tabpy-port <TabPy port> --tabpy-username <username> --tabpy-password <password> --tabpy-connect-timeout-ms <TabPy connect timeout>

Step 2: For secure connection use – {maestro-tabpy-secure} and for unsecure connection use – {maestro-tabpy}.

Step 3: You have to specify the certificate path by using -cf<certificate file path> if you selected secure mode.

Step 4: Specify the timeout setting in the variable --tabpy-connect-timeout-ms <TabPy connect timeout> in ms.

Step 5: To disconnect TabPy connection use the command - tsm security maestro-tabpy-ssl disable.

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Part 2: Create Python Script to Connect Tableau Server

Below is the snippet of the function that connects to Tableau Prep Builder and calls the data. The process takes a data frame as input and outputs data frames.

def encode(input):     
  le = preproces2ing.LabelEncoder()
  return pd.DataFrame({
    'Opportunity Number' : input['Opportunity Number'],
    'Supplies Subgroup Encoded' : le.fit_transform(input['Supplies Subgroup']),
    'Region Encoded' : le.fit_transform(input['Region']),
    'Route To Market Encoded' : le.fit_transform(input['Route To Market']),
    'Opportunity Result Encoded' : le.fit_transform(input['Opportunity Result']),
    'Competitor Type Encoded' : le.fit_transform(input['Competitor Type']),
    'Supplies Group Encoded' : le.fit_transform(input['Supplies Group']),

The following data types are supported:

Data type in Tableau Prep BuilderData type in Python
StringStandard UTF-8 string
DateDate in the format “YYYY-MM-DD”
DateTimeDate time in the format “YYYY-MM-DDT:HH:mm:ss” with zone offset (optional)

If you wish to return different fields that you get in input, a function named get_output_schema needs to be implemented to define output data types. 

Following syntax can be used to specify the data types:

Function in PythonResulting data type
prep_string ()String
prep_decimal ()Decimal
prep_int ()Integer
prep_bool ()Boolean
prep_date ()Date
prep_datetime ()DateTime

Example for get_output_schema function:

def get_output_schema():       
  return pd.DataFrame({
    'Opportunity Number' : prep_int(),
    'Supplies Subgroup Encoded' : prep_int(),
    'Region Encoded' : prep_int(),
    'Route To Market Encoded' : prep_int (),
    'Opportunity Result Encoded' : prep_int (),
    'Competitor Type Encoded' : prep_int()
    'Supplies Group Encoded' : prep_int()

Part 3: Connect Tableau Python (TabPy) Server

Step 1: To connect to the TabPy server, launch Tableau Python (TabPy) server.

Step 2: Click on Help > Settings and Performance > Manage Analytics Extension Connection.

Step 3: In the drop-down list, Select an Analytics Extension drop-down list, select Tableau Python (TabPy) Server.

Step 4: Provide the credentials as below:

  • The default port would be 9004.
  • Provide the username and password.
  • Specify the SSL certification if your server uses it.
  • On clicking Sign In, it will test the provided credentials, and if the credentials are valid, it will successfully sign in. Otherwise, it will throw an error.

Part 4: Add a Script to Your Flow

Step 1: TabPy requires an extension named tornado 5.1.1 version. 

Step 2: Run the below command to check the installed tornado version and install the correct version:

pip list
//If the installed version is not 5.1.1, then run the following command to uninstall the package.
pip uninstall tornado
pip install tornado=5.1.1

Step 3: Start your TabPy server and open the Tableau prep builder.

Step 4: Click on the Add connection button.

Step 5: From the Home page, click on Create > Flow to connect to data.

Step 6: Select the file type or server from the list of connectors.

Step 7: Click the plus icon, and select Add Script from the context menu

Step 8: In the connection type section, select Tableau Python (TabPy) Server.

Step 9: In the File Name section, click Browse to select the script file.

Step 10: Enter the Function Name; then press Enter to run your Script.

That’s it; now you have connected Python with Tableau Server using TabPy.


In this blog post, we have discussed how to connect Tableau using Python (TabPy) to perform various business transformations.

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