Data visualization aims to present the data into a more straightforward representation, such as scatter plot, density plot, bar chart, etc. It is also useful to give readers or analysts a global picture of their data. By visualizing your data, you can detect potential outliers. In Python, you can use various modules or libraries to visualize data. One of the mainstream modules is Matplotlib. You can visualize data using Matplotlib in various plotting styles. But, Matplotlib can not show you a dynamics plot. If you want to create a tremendous dynamic plot, you can use Dash from plotly (I hope to finish a story about a complete tutorial with Dash next month).

This story will guide you on how to visualize data with Matplotlib in a various way. 90 examples maybe can inspire you to create a plot from different points of view. It is not the most completed tutorials in data visualization with Matplotlib, but I believe that this story can accommodate many people’s needs, reach many disciples to be applied.

As I mentioned before, I will guide you through creating 90 various plot examples. The examples are distributed in 11 different style plots: scatter plot, line plot, histogram 1D, 2D, marginal plot, bar chart, box plot, violin plot, pie chart, polar chart, geographic projection, 3D plot, and contour plot. You can see Figure 1 to have a general idea of this story.

In this story, I try to focus on creating and customizing various plots. So, I assume you have known several techniques outside it, e.g., creating multiple subplots and customizing colormaps in Matplotlib. If you have not known it, I will give you some links to understand it.

At the beginning of writing this story, I plan to write only 1 story. But, I think I need to divide it into several parts because of the reading time. If I write it all in one story, it will cost more than 40 minutes. I guess you will be bored to read it all just in one story :). So, I divide it into 2 or 3 parts. I will limit the reading time to less than 30 minutes. This is the first part. Let’s start it.

#python #data-analysis #programming #data-science

1.45 GEEK