The matplotlib cheat sheet was created to help visualize data.
Datacamp provides a cheat sheet describing the basics of
seaborn. Seaborn is also a widely used library for data visualization with python. It allows getting a very clean chart with less code.
Matplotlib is a plotting library for the Python programming language. The most used module of Matplotib is Pyplot which provides an interface like Matlab but instead, it uses Python and it is open source.
In this note, we will focus on basic Matplotlib to help visualize our data. This is not a comprehensive list but contains common types of data visualization formats. Let’s hop to it!
The structure of this note:
Bokeh prides itself on being a library for interactive data visualization
Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive visualization library. That is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
Bokeh is also known for enabling the high-performance visual presentation of large data sets in modern web browsers.
For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily.
But there are also other advantages. Such as the various output options and the fact that you can embed your visualizations in applications.
And let’s not forget that the wide variety of visualization customization options makes this Python library an indispensable tool for your data science toolbox.
Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course. And that still want a handy one-page reference or for those who need an extra push to get started.
In short, you’ll see that this cheat sheet not only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts.