Realtime_Teableau_JupyterNotebook

Real-time connection between Tableau and Jupyter Notebook allowing for dynamic access to data

Jupytab has two components; Jupytab and Jupytab-server:

We will be installing each component in a separate environment

1. Setup jupytab notebook environment:

• In Terminal, we first create a virtual environment. Run the commands:

conda create -n jupytab-notebook-env python=3.9.6

conda activate jupytab-notebook-env

• Install jupytab and the ipykernel library to make our Jupyter kernel available in notebooks:

conda install jupytab=0.9.11

conda install ipykernel

python -m ipykernel install --user --name jupytab-notebook-env --display-name "jupytab-outlook-python-tableau"

here "--name" is the name of the env we want to install the kernel to, then "--display-name" is the kernel name we want to see inside jupyter notebook

• Install and launch jupyter notebook:

conda install jupyter

jupyter notebook

• In Jupyter Notebook, open the new kernel and run the following code to test that jupytab has been installed correctly

import jupytab
print(jupytab.__version__)

2. Setup jupytab-server environment:

• In terminal, deactivate current running environment: conda deactivate
• Create new server environment: conda create -n jupytab-server-env python conda activate jupytab-server-env
• Install jupytab-server: conda install jupytab-server

3. In Jupyter Notebook

In your new Jupyter Notebook kernel we created, Upload your own dataframe/frames that you're trying to push into Tableau. I will be using this project as a sequel to my Fetching-attachements-Microsoft-Outlook repository to form a real-time 3-way connection between my Outlook-365 email, Python Jupyter Notebook, and Tableau Server.

4. Connect to Tableau Server

• Load the dataframe in a Jupytab Tables dictionary, to indicate that we want to expose it to Tableau:

tables = jupytab.Tables()
tables['Data'] = jupytab.DataFrameTable("Data", dataframe=Data, include_index=True)
tables

Expose tables schema:- In order to allow jupytab-server to retrieve data

• generates a schema that declares all our Dataframes

# GET /schema
tables.render_schema()

• Where data is exported:

In Terminal: conda install flask

In Jupyter Notebook:

# GET /data
try:
    tables.render_data(REQUEST)
except NameError:
    print("Not available outside jupytab context")

5. Configure and Launch the Jupytab server

In the Jupytab server environment, we need to create a configuration file that will allow us to configure a few parameters like the server port, a secret token and of course the list of notebooks that the server must exposes. The config.ini file tells Jupytab which notebooks contain the tables that should be published for Tableau

• Write a config.ini file in any text editer. Put it anywhere you want. preferrably in the same environment you're working in:

listen_port = 8123
notebooks = SalesData_Outlook_Python_Tableau

[YOUR_PYTHON_NOTEBOOK_NAME]
path = /Users/PATH_TO_YOUR_PYTHON_NOTRBOOK/YOUR_PYTHON_NOTEBOOK.ipynb
description = GIVE THE CONNECTION A BRIEF DESCRIPTION

• in terminal: In the jupytab server environment, Launch the jubytab:

jupytab --config=/Users/PATH_TO_YOUR_config_FILE/config.ini

The ouput contains two important pieces of information:

1. The list of published notebooks.
2. The URL to be used in Tableau in order to access the data (including any security token declared in the configuration file).

NOTE: this migh give a jupytab command not found in terminal. this is because the intended command you’re attempting to use is located in a nonstandard directory or in another location (/usr/local/sbin/ etc).

Quick Fix:

• a. In terminal run print out the current paths in use:

(jupytab-server-env) echo $PATH

• b. add the new directory (where jupytab exists) to our path lits using:

(jupytab-server-env) export PATH=$PATH:/Users/YOUR_JUPYTAB_PATH

• back to ourout put from launching Jupytab:

Grab the link from the output