![]() ![]() If you are new to Python and want to set up the environment on your local machine, install Anaconda. It comes with the necessary Python packages for data science tasks. We will be using DataCamp’s Workspace for running the Python code. Furthermore, we will clean the data and export it into CSV file format. In our case, we will be focusing on the user profile and converting it into a readable Pandas dataframe. In the first part of the tutorial, we will learn to use Goodreads API to access public data. Check out the link below to access the code and the Tableau dashboard.ĭata Ingestion and Processing with Python This is a code-based step-by-step tutorial on Goodreads API and creating complex visualization on Tableau. 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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