55  Other Plotting Libraries

55.1 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.

55.2 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")

55.2.1 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")

55.2.2 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")

55.3 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!