Visualization with plotnine

Visualization with plotnine

In the last chapter, you used siuba to answer some questions about the music_top200 dataset.

You’ve been able to..

• filter for particular observations.
• arrange to find the highest or lowest values.
• mutate to add new columns.

However, most of the code so far has only printed out results as a table. Often a better way to understand and present this kind of data is as a graph.

In this chapter, you’ll learn the essential skill of data visualization using the plotnine package.

Visualizing with plotnine

In particular, this chapter will show you how to create scatterplots, like the one you see here.

Scatterplots compare two variables on an x- and y- axis.

Visualization and data wrangling are often intertwined, so you’ll see how the siuba and plotnine packages work closely together to create informative graphs.

Importing plotnine

from siuba import *
from plotnine import *

You’ll be creating plots using the plotnine package.

Just like the siuba package, you’ll have to load it with import.

Variables

billie = (
  track_features
  >> filter(_.artist == "Billie Eilish")
)

In this chapter, you’ll sometimes be visualizing subsets of the track_features data. For example, this code gets only tracks by the artist Billie Eilish.

When you’re working with just that subset, sometimes it’s useful to save the filtered data, as a new data frame.

To do this, you use the assignment operator.

Click down for a breakdown of assignment

Variables

(
  track_features
  >> filter(_.artist == "Billie Eilish")
)

First, write out the pipe with the filter verb.

This is the same as you did in the previous chapter.

Note that whether the name of the data, track_features is right after the ( or on its own line, the code works the same.

Variables

billie = (
  track_features
  >> filter(_.artist == "Billie Eilish")
)

Then, write the variable name, followed by the variable operator (an equal sign).

In this case, the variable name is billie.

Variables (result)

billie

	artist	album	track_name	energy	valence	danceability	speechiness	acousticness	popularity	duration
1273	Billie Eilish	dont smile ...	my boy	0.3940	0.3240	0.692	0.2070	0.472	44	170.852
2899	Billie Eilish	WHEN WE ALL...	listen befo...	0.0561	0.0820	0.319	0.0450	0.935	79	242.652
2950	Billie Eilish	lovely (wit...	lovely (wit...	0.2960	0.1200	0.351	0.0333	0.934	89	200.186
...	...	...	...	...	...	...	...	...	...	...
24857	Billie Eilish	WHEN WE ALL...	ilomilo	0.4230	0.5720	0.855	0.0585	0.724	79	156.371
24997	Billie Eilish	WHEN I WAS ...	WHEN I WAS ...	0.3320	0.0628	0.696	0.0425	0.853	71	270.520
25147	Billie Eilish	come out an...	come out an...	0.3210	0.1770	0.640	0.0931	0.693	74	210.376

27 rows × 10 columns

Now if you print the billie dataset, we can see that it’s another table.

But this one has only 27 rows: one for each track by Billie Eilish in the original data.

Visualizing with plotnine

(billie
 >> ggplot(aes("energy", "valence"))
  + geom_point()
  + labs(title = "Billie Eilish hit track features")
)

Suppose you want to examine the energy and valence of Billie Eilish’s songs.

You could do this with a scatterplot comparing two variables in our track_features dataset: energy on the x-axis and valence on the y-axis.

There are three parts to a plotnine graph.

(Click the down button to see a breakdown of the code)

Visualizing with plotnine

(billie
 >> ggplot(aes("energy", "valence"))
  + geom_point()
  + labs(title = "Billie Eilish hit track features")  
)

First is the data that we’re visualizing. In this case, that is the billie variable you just created.

Visualizing with plotnine

(billie
 >> ggplot(aes("energy", "valence"))
  + geom_point()
  + labs(title = "Billie Eilish hit track features")
)

Second is the mapping of variables in your dataset to aesthetics in your graph.

An aesthetic is a visual dimension of a graph that can be used to communicate information.

In a scatterplot, your two dimensions are the x axis and the y axis, so you write aes (for “aesthetic”), parentheses, x equals gdpPerCap, y = lifeExp, telling it which variables to place on which axes.

Visualizing with plotnine

(billie
 >> ggplot(aes("energy", "valence"))
  + geom_point()
  + labs(title = "Billie Eilish hit track features")
)

The third step is specifying the type of graph you’re creating. You do that by adding a layer to the graph: use a plus after the ggplot, and then geom underscore point.

The “geom” means you’re adding a type of geometric object to the graph, the “point” indicates it’s a scatter plot, where each observation corresponds to one point.

Together, these three parts of the code–the data, the aesthetic mapping, and the layer–construct the scatter plot you see here.

Let’s practice!

Scroll down to get started with practice!