# A simple cheat sheet for Seaborn Data Visualization

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`

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

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

`sns.barplot`

-are useful for comparing quantities corresponding to different groups.**Bar charts**`sns.heatmap`

-can be used to find color-coded patterns in tables of numbers.**Heatmaps**`sns.scatterplot`

-show the relationship between two continuous variables; if color-coded, we can also show the relationship with a third categorical variable.**Scatter plots**`sns.regplot`

- Including ain the scatter plot makes it easier to see any linear relationship between two variables.**regression line**`sns.lmplot`

- This command is useful for drawing multiple regression lines, if the scatter plot contains multiple, color-coded groups.`sns.swarmplot`

-show the relationship between a continuous variable and a categorical variable.**Categorical scatter plots**

** 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`

-show the distribution of a single numerical variable.**Histograms**`sns.kdeplot`

-(or**KDE plots**) show an estimated, smooth distribution of a single numerical variable (or two numerical variables).**2D KDE plots**`sns.jointplot`

- This command is useful for simultaneously displaying a 2D KDE plot with the corresponding KDE plots for each individual variable.