Build accurate, engaging, and easy-to-generate data visualizations using the popular programming language Python.
Overview
Syllabus
Introduction
- Effectively present data with Python
- Before you start
- Using the exercise files
- Value of data visualization
- Leverage programming languages
- Overview of Jupyter Notebooks
- 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
- 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
- Create heatmaps
- Create histograms
- Create subplots
- Next steps
Taught by
Michael Galarnyk and Madecraft
