Common Plots

Introduction

In this chapter, we’ll look at some of the most common plots that you might want to make–and how to create them using the most popular data visualisations libraries, including matplotlib, plotnine, seaborn, altair, and plotly. If you need an introduction to these libraries, see the previous chapter.

This chapter has benefited from viztech, a repository that aims to recreate the entire Financial Times Visual Vocabulary using plotnine, from the plotnine documentation, from the matplotlib documentation, from the seaborn documentation, from the altair documentation, from the plotly documentation, and from examples posted around the web on forums and in blog posts. It’s also worth noting that I’m more of an expert in matplotlib than anything else–I would really welcome contributions in the form of plots with particular libraries that I have not been able to find or implement myself.

Bear in mind that for many of the matplotlib examples, using the df.plot.* syntax can get the plot you want more quickly! To be more comprehensive, the solution for any kind of data is shown in the examples below.

Throughout, we’ll assume that the data are in a tidy format (one row per observation, one variable per column). Remember that all Altair plots can be made interactive by adding .interactive() at the end.

First, though, let’s import the libraries we’ll need.

import numpy as np
import pandas as pd
import seaborn as sns
import seaborn.objects as so
import matplotlib.pyplot as plt
from plotnine import *
import altair as alt
import plotly.express as px
from vega_datasets import data
import os
from pathlib import Path
import warnings

# Set seed for reproducibility
np.random.seed(10)
# Turn off warnings
warnings.filterwarnings('ignore')

Scatter plot

In this example, we will see a simple scatter plot with several categories using the “cars” data:

cars = data.cars()
cars.head()

	Name	Miles_per_Gallon	Cylinders	Displacement	Horsepower	Weight_in_lbs	Acceleration	Year	Origin
0	chevrolet chevelle malibu	18.0	8	307.0	130.0	3504	12.0	1970-01-01	USA
1	buick skylark 320	15.0	8	350.0	165.0	3693	11.5	1970-01-01	USA
2	plymouth satellite	18.0	8	318.0	150.0	3436	11.0	1970-01-01	USA
3	amc rebel sst	16.0	8	304.0	150.0	3433	12.0	1970-01-01	USA
4	ford torino	17.0	8	302.0	140.0	3449	10.5	1970-01-01	USA

Matplotlib

fig, ax = plt.subplots()
for origin in cars["Origin"].unique():
    cars_sub = cars[cars["Origin"] == origin]
    ax.scatter(cars_sub["Horsepower"], cars_sub["Miles_per_Gallon"], label=origin)
ax.set_ylabel("Miles per Gallon")
ax.set_xlabel("Horsepower")
ax.legend()
plt.show()

Seaborn

Note that this uses the seaborn objects API.

(
    so.Plot(cars, x="Horsepower", y="Miles_per_Gallon", color="Origin")
    .add(so.Dot())
)

Plotnine

(
    ggplot(cars, aes(x="Horsepower", y="Miles_per_Gallon", color="Origin"))
    + geom_point()
    + ylab("Miles per Gallon")
)

<ggplot: (8766531716798)>

Altair

For this first example, we’ll also show how to make the altair plot interactive with movable axes and more info on mouse-hover.

alt.Chart(cars).mark_circle(size=60).encode(
    x="Horsepower",
    y="Miles_per_Gallon",
    color="Origin",
    tooltip=["Name", "Origin", "Horsepower", "Miles_per_Gallon"],
).interactive()

Plotly

Plotly is another declarative plotting library, at least sometimes (!), but one that is interactive by default.

fig = px.scatter(
    cars,
    x="Horsepower",
    y="Miles_per_Gallon",
    color="Origin",
    hover_data=["Name", "Origin", "Horsepower", "Miles_per_Gallon"],
)
fig.show()