Ai+ Training

Day 1

Module 1: An overview of the 

Matplotlib Figure and Axes 

Training Overview 

Matplotlib Figure and Axes objects

An object-oriented approach to Matplotlib

Seaborn as a “wrapper” for Matplotlib

Module 2: Introduction to Seaborn 

Seaborn documentation and API

Integration with Pandas DataFrames

Main plotting parameters

Grid vs Axes plots

A different categorization for Seaborn plots

Module 3: Distribution Plots

Univariate distribution plots

Box plots

Changing the orientation of plots

Histograms and KDEs

Other distribution plots

Module 4: Automatic Grouping by Category 

Plotting a continuous by a categorical variable

Grouping with two continuous variables

Splitting groups by setting hue

Day 2

Module 5: Grouping and Aggregating Plots

Univariate grouping and aggregating

Choosing the aggregation function

Bar, Point, Count, and Line plots

Module 6: Tidy Data 

Definition of tidy data

Long data vs wide data

Seaborn automatically groups and aggregates

Manually grouping and aggregating with pandas

Module 7: Raw Data Plots 

Scatterplots

Regression plots

Heatmaps

Module 8: Grid Plots

Relationship with Matplotlib Figures

Discovering the Grid plotting functions

Setting the plot with the kind parameter

Creating multiple Axes with row/col parameters

Bivariate distribution plots

Hierarchical cluster maps

Module 9: Seaborn Styles and Palettes 

Run configuration parameters

Specific Seaborn styles

Color palettes and widgets