Plotly Adventure

My goal is to learn new packages related to visualizations and dashboarding with python.  An overwhelming vote by the internet says, "learn plotly!" As a result, I asked GPT-4, what are things to know about plotly, and this is the response. I plan to follow each section and provide a post diving into each topic. 

As an experienced programmer with years of experience with Plotly, I would guide you through the main principles and learning steps as follows:

Introduction to Plotly: Plotly is an open-source graphing library that makes it easy to create interactive, publication-quality visualizations in Python, R, and other programming languages. It provides a high-level interface for drawing attractive and informative statistical graphics.

Understanding Plotly components: Familiarize yourself with the core components of Plotly, such as Plotly Express (a high-level interface for creating common chart types quickly) and Graph Objects (a low-level interface for more customizable charts).

Basic chart types: Start by learning how to create basic chart types like scatter plots, line charts, bar charts, and pie charts using Plotly Express.

    Basic Bar Chart

Customizing visualizations: Learn how to modify and customize chart elements such as titles, axis labels, legends, and colors.

Interactivity: Explore Plotly's interactive capabilities, such as hover tooltips, zooming, and panning.

Advanced chart types: Gradually dive into more advanced chart types, like heatmaps, contour plots, box plots, and violin plots.

Multi-plot layouts: Learn how to create multi-plot layouts, which allow you to display multiple charts within a single visualization.

Integrating with Dash: Discover how to use Plotly with Dash, a web application framework, to create interactive dashboards and web applications.

Creating interactive dashboards in Google Colab is straight forward.  The next goal is to create a true "dash" that is not notebook based.

    Google Colab Example

Exporting and sharing: Understand the different options for exporting your visualizations as images or interactive HTML files and sharing them with others.

Best practices: Learn about best practices for creating effective visualizations, such as choosing the right chart type for your data and ensuring visual clarity.

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