Just gonna put this out here, courtesy of Kaggle's Data Visualization course.

It is a super simple description of the different plots you can do with Seaborn, simply divided into the type of story you're trying to tell.

There's even an awesome photo!

Trends - A trend is defined as a pattern of change.

• `sns.lineplot` - Line charts are best to show trends over a period of time, and multiple lines can be used to show trends in more than one group.

Relationship - There are many different chart types that you can use to understand relationships between variables in your data.

• `sns.barplot` - Bar charts are useful for comparing quantities corresponding to different groups.
• `sns.heatmap` - Heatmaps can be used to find color-coded patterns in tables of numbers.
• `sns.scatterplot` - Scatter plots show the relationship between two continuous variables; if color-coded, we can also show the relationship with a third categorical variable.
• `sns.regplot` - Including a regression line in the scatter plot makes it easier to see any linear relationship between two variables.
• `sns.lmplot` - This command is useful for drawing multiple regression lines, if the scatter plot contains multiple, color-coded groups.
• `sns.swarmplot` - Categorical scatter plots show the relationship between a continuous variable and a categorical variable.

Distribution - We visualize distributions to show the possible values that we can expect to see in a variable, along with how likely they are.

• `sns.distplot` - Histograms show the distribution of a single numerical variable.
• `sns.kdeplot` - KDE plots (or 2D KDE plots) show an estimated, smooth distribution of a single numerical variable (or two numerical variables).
• `sns.jointplot` - This command is useful for simultaneously displaying a 2D KDE plot with the corresponding KDE plots for each individual variable.