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 - 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.
Martin H. Normark

Martin H. Normark

Product and UX Hacker. Web and iOS developer.