Data wrangling and visualization with Python

Visualizing data with Seaborn

Overview

Teaching: 10 min
Exercises: 2 min
Questions

  • How can I use Seaborn to make more complex data visualizations?

Objectives

  • Explain how to visualize pandas DataFrames with seaborn.

  • Explain how to rearranged pandas DataFrames.

Pandas has a wealth of plotting features (we haven’t even scratched the surface yet), but sometimes you need a slightly more elaborate data visualization. For those times, there’s Seaborn (there are a lot of other Python visualization libraries too, but Seaborn is possibly the easiest).

Let’s start out by making a heatmap of inflammation over time by patient.

import seaborn as sns
sns.heaatmap(data, xticklabels=False, yticklabels=False)

It would be useful to have a plot that shows error around an average trajectory over time. We can accomplish this with Seaborn’s tsplot, but we need to re-arrange the data a little first. We need to have all of our information about what time point a given inflammation value was taken at in a single column. This will take a few steps: ~~~ data = pandas.concat(frames) data = data.set_index(pandas.Series([i for i in range(720)])) data.reset_index(level=0, inplace=True) data = pandas.melt(data, id_vars=[“index”]) data.columns = [“patient”, “day”, “inflammation”] ~~~

Now we’re ready to make the plot:

sns.tsplot(data=data, time="day", value="inflammation", unit="patient")

Making a histogram

Seaborn has a function called distplot that makes a histogram. See if you can use the documentation to figure out how to make a histogram of the various inflammation values.

Solution

sns.distplot(data["inflammation"])

Key Points