CS250: Python for Data Science

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_context('paper')
sns.set_style('white', {'font.family':'Futura', 'text.color':'1'})

mtcars = pd.read_csv('data/mtcars.csv')

Static plots

We will demonstrate plotting in what I'll call the matplotlib ecosystem. matplotlib is the venerable and powerful visualization package that was originally designed to emulate the Matlab plotting paradigm. It has since evolved and has become a bit more user-friendly. It is still quite granular and can facilitate a lot of custom plots once you become familiar with it. However, as a starting point, I think it's a bit much. We'll see a bit of what it can offer later.

We will consider two other options, which are built on top of matplotlib, but are much more accessible. These are pandas and seaborn. The two packages have some different approaches, but both wrap matplotlib in higher-level code and decent choices, so we don't need to get into the matplotlib trenches quite so much. We'll still call matplotlib in our code since both these packages need it for some fine-tuning. Both packages are also very much aligned to the DataFrame construct in pandas, which makes plotting a much more seamless experience.

mtcars.plot.scatter(x = 'hp', y = 'mpg');
plt.show()
# mtcars.plot(x = 'hp', y = 'mpg', kind = 'scatter');

sns.scatterplot(data = mtcars, x = 'hp', y = 'mpg');
plt.show() 

from plotnine import *
(ggplot(mtcars) + 
  aes(x = 'hp', y = 'mpg') +
  geom_point())

<ggplot: (302395799)>

Dynamic or interactive plots

There are several Python packages that wrap around Javascript plotting libraries that are so popular in web-based graphics like D3 and Vega. Three that deserve mention are plotly, bokeh, and altair.

plotly is a Python package developed by the company Plot.ly to interface with their interactive Javascript library either locally or via their web service. Plot.ly also develops an R package to interface with their products as well. It provides an intuitive syntax and ease of use and is probably the more popular package for interactive graphics from both R and Python.

import plotly.express as px
fig = px.scatter(mtcars, x = 'hp', y = 'mpg')
fig.show()

from bokeh.plotting import figure, output_file
from bokeh.io import output_notebook, show
output_notebook()
p = figure()
p.xaxis.axis_label = 'Horsepower'
p.yaxis.axis_label = 'Miles per gallon'
p.circle(mtcars['hp'], mtcars['mpg'], size=10);

show(p)

import altair as alt

alt.Chart(mtcars).mark_point().encode(
    x='hp',
    y='mpg'
).interactive()