Pandas is one of the most popular data analysis libraries for Python. In this
course, you will learn how to create a wide range of plots for your data,
and how to customize them to make them both attractive and informative
for your audience.
At some point when you are working with a dataset, you will want to
make the properties of that dataset visible in a graphical way. This is
a core skill for every data scientist or data engineer. In this course, Pandas
Playbook: Visualization, you’ll learn how to create a large variety
of beautiful plots with Pandas, one of the most popular data analysis libraries for Python. First, you'll learn the very basics of plotting with pandas, learning how to prepare your dataset for plotting, and how to create
common plots like a bar, line, and scatter plot. Next, you will explore
matplotlib, the Python library that generates the actual graphics, how this interacts with Pandas, and how to use it correctly. Then, you will go
more in-depth and learn about all the ways to customize your plots,
including line styles, colors and themes, customizing axes and legends,
creating interactive plots, and much more. Finally, you will see a short
overview of two other visualization libraries that can be used with
Pandas: Seaborn, which is focused on statistical plotting, and Bokeh,
which can create interactive visuals for the web. After watching this course, you’ll have a deep understanding of all possible ways you can use Pandas to visualize your data. You'll know how to write efficient and clear code that creates beautiful plots, following best practices. This course will also make you more proficient in
exploring datasets and communicating your results with others.
After years of working in software development, Reindert-Jan Ekker has
decided to pursue another passion of his: education. He currently
works as a college professor of Computer Science in the Netherlands,
teaching many subjects like web development, algorithms and data
structures and Scrum.