Building visualizations with Seaborn

Introduction to Python in Power BI

Jacob H. Marquez

Data Scientist

Plots with the Seaborn package

A line plot showing flights data by year and colored by month.

¹ https://seaborn.pydata.org/generated/seaborn.lineplot.html

A pair plot with four columns and four rows.

¹ https://seaborn.pydata.org/generated/seaborn.pairplot.html

A joint plot showing bill length vs bill depth.

¹ https://seaborn.pydata.org/generated/seaborn.jointplot.html

A histogram of the heights of people. It is a normal distribution.

¹ https://seaborn.pydata.org/generated/seaborn.histplot.html

Histogram skewed to the right with most observations on the left side.

Histogram skewed to the left with most observations on the right side.

Histogram of a bimodal distribution

Random blob of data with no relationship.

A scatter plot with a positive relationship, which means, it moves to the right and up.

A scatter plot with a negative relationship, which means, it moves to the right and down.

A joint plot with a scatter plot in the middle and two histograms, one on the top and right side.

seaborn.jointplot(
    x = "x",
    y = "y", 
    data = DataFrame
    )

A pair plot with four columns and four rows.

seaborn.pairplot(
    data = DataFrame
    )

import matplotlib.pyplot as plt
import seaborn as sns

# Data transformation steps
...

seaborn.___plot(
    data = DataFrame,
      ...
    )

plt.show()

Introduction to Python in Power BI