In ancient Indian texts, explanation of philosophical concepts often begin by negating what that concept is not about. Utilizing a reoccurring phrase neti neti (meaning_ neither this nor that_), the idea is that telling what something is not, is at least as important as explaining the actual meaning of that concept/idea. Following in the footsteps of these ancient philosophers, let me begin by enumerating what** this article is not about**:

• The article is not about how to quickly make charts in plotly, often with just a single line of code as in the case of plotly express. If that is what you are interested in, please follow this amazing medium article by Will Koehrsen.
• This article is also not about listing all the different chart types that are available for data visualization. If that is what you are looking for, check out this extremely informative article Samantha Lile. In fact this article discusses only two different chart types : line and scatter.

Introduction

Any data analysis project has two essential goals. First, to curate data in readily interpretable form, uncover hidden patterns, and identify key trends. Second, and perhaps more important, is to effectively communicate these findings to the readers through thoughtful data visualization. This is an introductory article on how to begin thinking about customized visualizations that readily disseminate key data features to the viewer. We achieve this by moving beyond the one line charts that have made plotly so popular among data analysts and focusing on individualised chart layouts & aesthetics**.**

All code used in this article is available on Github. All charts presented here are interactive and have been rendered using jovian, an incredible tool for sharing and managing jupyter notebooks. This medium article by Usha Rengaraju contains all the details on how to use this tool.

#python #charts #data-analysis #data-visualization #plotly #data-science