NB: GGPlot in Python with Plotnine

GGPlot in Python

There are two ports of GGPlot2 to Python: pygg and plotnine.

The first seems to have stopped development and is much less used.

Let’s look at Plotnine.

## ! conda install -c conda-forge plotnine -y

import pandas as pd
import numpy as np
from pandas.api.types import CategoricalDtype

from plotnine import *
from plotnine.data import mpg

Our old friend, mpg in Python:

mpg

	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
4	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact
...	...	...	...	...	...	...	...	...	...	...	...
229	volkswagen	passat	2.0	2008	4	auto(s6)	f	19	28	p	midsize
230	volkswagen	passat	2.0	2008	4	manual(m6)	f	21	29	p	midsize
231	volkswagen	passat	2.8	1999	6	auto(l5)	f	16	26	p	midsize
232	volkswagen	passat	2.8	1999	6	manual(m5)	f	18	26	p	midsize
233	volkswagen	passat	3.6	2008	6	auto(s6)	f	17	26	p	midsize

234 rows × 11 columns

A Simple Bar Chart

(ggplot(mpg)            # defining what data to use
    + aes(x='class')    # defining what variable to use
    + geom_bar(size=20) # defining the type of plot to use
)

<Figure Size: (640 x 480)>

Notice that aes() is not a helper function (a function in the argument space).

Also, R dots become _ in the argument names.

Note that we don’t have to use the syntax above, which groups the functions in a single expression with (...).

We can do this:

ggplot(mpg) + aes(x='class') + geom_bar(size=20)

<Figure Size: (640 x 480)>

Or this:

ggplot(mpg) + \
    aes(x='class') + \
    geom_bar(size=20)

<Figure Size: (640 x 480)>

Note that none of these are like R due to differing white space rules.

Faceting

ggplot(mpg) + \
    aes(x = 'drv', y = 'cty', color = 'class', size='cyl') + \
    geom_point()

<Figure Size: (640 x 480)>

(ggplot(mpg)         
 + aes(x='drv', y='cty', color='class', size='cyl')
 + geom_point()
 + facet_wrap('class')
 + theme(legend_position = "none")
)

<Figure Size: (640 x 480)>

The Pandas Way

Note the GGPlot included the computation of counts in the geom_bar() function.

ggplot(mpg) + aes(x='class') + geom_bar(size=20)

<Figure Size: (640 x 480)>

%matplotlib inline

# mpg['class'].plot.bar()

mpg['class'].value_counts().sort_index().plot.bar(rot=45);

However, sometomes Pandas does do internal calculations, as with .hist():

mpg['cty'].hist();

For faceting in Pandas, see this: https://stackoverflow.com/questions/29786227/how-do-i-plot-facet-plots-in-pandas

Notice that it is essentially the result of a .groupby() followed by .unstack().

So, Pandas expects you to do the data transformations upfront.

GGPlot2 will handle these in the geometries and facets

GGPlot2 is easier, but Pandas separates comcerns

As a rule, data operations should never take place in the visualization.