Data Visualization: Matplotlib Seaborn Plotly and Altair

The section on Pandas gives you an overview of the data cleaning, data processing, and data manipulation operations you can do with Pandas. These operations include changing data types, getting summary information about your dataset, and getting aggregate values.

In the section on Matplotlib, one of the most popular plotting libraries, you learn about figure and axes level objects and some of the most common plots that show distributions and relationships between variables, or changes in a variable over time, as well as how to make these types of plots.

You learn how to make categorical, relational, numerical and distribution plots, as well as how to use color and size to show a lot of information. This can help you with visual data exploration and data storytelling.

The section on Altair gives you an overview of another powerful Python Visualization library that has a wide range of plotting abilities. You can use Altair to make the most common plots as well as more complex visualizations.

The end of this class:

When you finish this class, you will have a better idea of how different types of data work.

You will be able to look at summary data about your dataset and see how variables are linked together.

You learn how to talk about your data with different people at different levels of detail, depending on where they are in the decision-making chain.

It’s important to note that we use Google Colab in this class.