How To Prepare Your Data For Visualizations

Want to get started on your next Data Visualization project? Start off by getting friendly with Data Cleaning. Data Cleaning is a vital step in any data pipeline, transforming raw, ‘dirty’ data inputs into those that are more reliable, relevant and concise. Data preparation tools such as Tableau Prep or Alteryx were created for this purpose, but why spend money on these services when you can accomplish the task with open-source programming languages like Python? This article will guide you through the process of getting data ready for visualization using Python scripts, offering a more cost-effective alternative to data preparation tools. <blockquote> Note: Throughout this article we will be focusing on getting data Tableau ready for data visualizations, but the main concepts equally apply to other business intelligence tools. </blockquote> I get it. Data cleaning just seems like another step in the already lengthy process of bringing your visualizations or dashboards to life. But it’s crucial, and can be enjoyable. It’s how you get comfortable with your data set, by getting an in-depth look at the data that you have and don’t have, and the consequential decisions you have to take to approach your end analysis goals. Whilst Tableau is a versatile data visualization tool, sometimes the route to get to your answer isn’t clear. This is where processing your dataset before loading it into Tableau may be your biggest secret helper. Let’s explore some key reasons why data cleaning is beneficial before integrating it with Tableau <a href="https://towardsdatascience.com/how-to-prepare-your-data-for-visualizations-94e33473f70b">Click Here</a>