Putting it all together

Introduction to Data Visualization with Seaborn

Erin Case

Data Scientist

Getting started

To import Seaborn:

import seaborn as sns

To import Matplotlib:

import matplotlib.pyplot as plt

To show a plot:

plt.show()

sns.relplot(x="x_variable_name", 
            y="y_variable_name", 
            data=pandas_df, 
            kind="scatter")

Show the distribution of a quantitative variable within categories defined by a categorical variable
Examples: bar plots, count plots, box plots, point plots

sns.catplot(x="x_variable_name", 
            y="y_variable_name", 
            data=pandas_df, 
            kind="bar")

Setting hue will create subgroups that are displayed as different colors on a single plot.

Scatter plot with hue

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

Setting row and/or col in relplot() or catplot() will create subgroups that are displayed on separate subplots.

Scatter plot with subplots

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

Object Type	Plot Types	How to Add Title
`FacetGrid`	`relplot()`, `catplot()`	`g.fig.suptitle()`
`AxesSubplot`	`scatterplot()`, `countplot()`, etc.	`g.set_title()`

Add x- and y-axis labels:

g.set(xlabel="new x-axis label",
      ylabel="new y-axis label")

Rotate x-tick labels:

plt.xticks(rotation=90)

Introduction to Data Visualization with Seaborn