54  Other Plotting Libraries

54.1 Plotnine

If you’ve come from R and made use of the ggplot library, you might like plotnine!

This library makes use of the grammar of graphics to build graphs up in steps.

Tip

In your own environment on your local machine, you will need to run

pip install plotnine

from plotnine import ggplot, geom_line, aes
from plotnine.data import mtcars
import pandas as pd

data = pd.DataFrame()
data["time"] = [11,12,23,31,46,52,61,72,83]
data["count"] = [23,61,65,81,60,15,29,40,42]


(
    ggplot(data, aes("time", "count"))
    + geom_line()
)

Plotnine makes it easy to split data over multiple plots by a category.

This example shows a plot of the numbers of miles per gallon achieved by cars with a different number of horsepower, split by flat engines (0) and v-shaped engines (1).

from plotnine.data import mtcars

mtcars.head()

	name	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
0	Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
1	Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
2	Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
3	Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
4	Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2

from plotnine import ggplot, geom_point, aes, stat_smooth, facet_wrap
(
    ggplot(mtcars, aes("hp", "mpg", color="factor(vs)"))
    + geom_point()
    + stat_smooth(method="lm")
    + facet_wrap("vs")
)

You can read more about the package, and see more code examples, here.

54.2 Plotly

Plotly is a very powerful library for creating interactive graphs.

There are several different ways to create plotly graphs, with the more complex methods giving you more control over the end product.

However, the plotly.express module is a great place to start.

import pandas as pd
import plotly.express as px

data = pd.DataFrame()
data["time"] = [11,12,23,31,46,52,61,72,83]
data["count"] = [23,61,65,81,60,15,29,40,42]

data.head()

	time	count
0	11	23
1	12	61
2	23	65
3	31	81
4	46	60

px.line(data, x="time", y="count")

54.2.1 Bar Chart

data = pd.DataFrame()
data["hsma_trainers"] = ["Dan", "Sammi", "Amy"]
data["hours_of_teaching"] = [96, 54, 6]

data

	hsma_trainers	hours_of_teaching
0	Dan	96
1	Sammi	54
2	Amy	6

px.bar(data, x="hsma_trainers", y="hours_of_teaching")

54.2.2 Scatter Plot

data = pd.DataFrame()
data["a"] = [11,52,61,72,83,12,23,31,46]
data["b"] = [23,61,65,81,60,15,29,40,42]

data

	a	b
0	11	23
1	52	61
2	61	65
3	72	81
4	83	60
5	12	15
6	23	29
7	31	40
8	46	42

px.scatter(data, x="a", y="b")

54.3 Seaborn

Seaborn builds on matplotlib. It’s good at creating many kinds of visualisations, but does things a little differently to matplotlib.

You can take a look at its documentation to see if you like the way it’s written.

If you’re struggling to make a graph in matplotlib, it’s worth checking to see if Seaborn provides this graph type instead!