Pandas Playbook: Visualization

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.
Course info
Rating
(17)
Level
Intermediate
Updated
Oct 9, 2018
Duration
2h 12m
Table of contents
Course Overview
Course Introduction
Making Simple Plots
Navigating the API Jungle: Matplotlib, Pandas, and the Jupyter Notebook
Creating Advanced Plots with Pandas
Visualizing Statistical Properties with Seaborn
Creating Interactive Plots for the Web with Bokeh
Description
Course info
Rating
(17)
Level
Intermediate
Updated
Oct 9, 2018
Duration
2h 12m
Description

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.

About the author
About the author

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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Reindert-Jan Ekker, and welcome to this playbook about visualizing your data with Pandas. I'm a senior developer and freelance educator, and in this course, I'll teach you about visualizing your data with Pandas, the most popular Python framework for doing data science and data analysis. When working with a dataset, being able to make attractive and informative visualizations is extremely important, for example when you're exploring the data or when you want to communicate your results with others. This course gives you an in-depth understanding of how to create beautiful plots for your dataset using Pandas. You will learn how to create common plot types like bar plots, area plots, scatter plots, and many more, how to customize the way your plots look using line styles, color themes, configuring axes and legends, etc., etc., how to prepare your dataset for plotting, and how to follow best practices, and we'll also see some other plotting libraries besides Pandas, namely Seaborn and Bokeh. By the end of this course, you'll be able to create most plots you will need in a common work flow using Pandas, customizing it to show the data in exactly the way you want and making it attractive too. Before beginning the course, you should be familiar with the very basics of Python, Pandas, and data science. I hope you'll join me on this journey to learn how to visualize your data with Pandas with this playbook course at Pluralsight.