Python for Data Visualization

Build accurate, engaging, and easy-to-generate data visualizations using the popular programming language Python.

Introduction

• Effectively present data with Python
• Before you start
• Using the exercise files

1. Data Visualization Overview

• Value of data visualization
• Leverage programming languages
• Overview of Jupyter Notebooks

2. Leverage pandas for Analysis

• Introduction to pandas
• Create sample data
• Load sample data
• Basic operations
• Simplify with slicing
• Filter and clean data
• Rename and delete columns
• Aggregate functions
• Identify missing data
• Remove or fill in missing data
• Convert pandas DataFrames
• Export pandas DataFrames

3. Simplify Visualization with Matplotlib

• Basics of Matplotlib
• Set marker type and colors
• MATLAB-style vs. object syntax
• Set titles, labels, and limits
• Add grids
• Create legends
• Save plots to files
• Create plots with Matplotlib wrappers

4. Customize Visualizations with Matplotlib

• Create heatmaps
• Create histograms
• Create subplots

Conclusion

• Next steps