Instead, we will first extract and clean the data in Python (Jupyter Notebook) and then use Tableau to create interactive visualization. We will not be using Tabpy to create a Tableau Python server and execute Python scripts within Tableau. Then, we will be using clean data to create data visualization on Tableau. In this tutorial, we are going to use Python for extracting and cleaning the data. Python is a multipurpose language, and using it with Tableau gives us the freedom to perform highly complex tasks. It provides you with machine learning frameworks, data orchestrations, multiprocessing, and rich libraries to perform almost any task possible. ![]() You can use it to extract, clean, process, and apply complex statistical functions to the data. Python is popular programming among the data community. Tableau is a powerful Business Intelligence (BI) tool, but there are limitations that's where Python language comes to the rescue. ![]() ![]() You can perform arithmetic, logical, spatial, and predictive modeling functions using calculated fields. Tableau provides several options to augment and create new data fields.
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