Build accurate, engaging, and easy-to-generate data visualizations using the popular programming language Python.
Overview
Syllabus
Introduction
- Effectively present data with Python
- What you should know before you start
- Using the exercise files
- Value of data visualization
- Why use a programming language?
- Overview of Jupyter Notebooks
- Introduction to pandas
- Create sample data
- Load sample data
- Basic operations
- Slicing
- Filtering
- Renaming and deleting columns
- Aggregate functions
- Identifying missing data
- Removing or filling in missing data
- Convert pandas DataFrames to NumPy arrays or dictionaries
- Export pandas DataFrames to CSV and Excel files
- Basics of Matplotlib
- Setting marker type and colors
- MATLAB-style vs. object syntax
- Setting titles, labels, and limits
- Grids
- Legends
- Saving plots to files
- Matplotlib wrappers (pandas and Seaborn)
- Heatmaps
- Histograms
- Subplots
- Next steps
Taught by
Michael Galarnyk and Madecraft
