Introduction to Altair
MIDS W209: Information Visualization

Andy Reagan | andy[at]andyreagan.com | @andyreagan
https://john-guerra.github.io/MIDS_W209_Information_Visualization_Slides/01_Introduction/altair.html
Partially based on slides from Jake VanderPlas

Core idea

Familiar tool:

Plotting with matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randn(1000) 
y = np.random.randn(1000) 
color = np.arange(1000)

plt.scatter(x, y, c=color) 
plt.colorbar()

Plotting with matplotlib: strengths

Designed like MatLab: switching was/is easy

Many rendering backends

Can reproduce just about any plot (with a bit of effort)

Well-tested, standard tool for 15 years

Gallery

Plotting with matplotlib: weaknesses

Designed like MatLab

API is imperative and often overly verbose

Poor/no support for interactive/web graphs

Plotting with matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(1000) 
y = np.random.randn(1000) 
color = np.arange(1000)

plt.scatter(x, y, c=color) 
plt.colorbar()

Statistical Visualization

from vega_datsets import data 
iris = data('iris') 
iris.head()
Data in column-oriented format; i.e. rows are samples, columns are features

Statistical Visualization: Grouping

color_map = dict(zip(iris.species.unique(), 
                     ['blue', 'green', 'red']))
                   
for species, group in iris.groupby('species'): 
    plt.scatter(group['petalLength'], group['sepalWidth'],
                color=color_map[species], 
                alpha=0.3, edgecolor=None, label=species)

plt.legend(frameon=True, title='species') 
plt.xlabel('petalLength') 
plt.ylabel('sepalLength')
  1. Data?
  2. Transformation?
  3. Marks?
  4. Encoding?
  5. Scale?
  6. Guides?

Statistical Visualization: Faceting

color_map = dict(zip(iris.species.unique(),
                     ['blue', 'green', 'red'])) 
n_panels = len(color_map)

fig, ax = plt.subplots(1, n_panels, figsize=(n_panels * 5, 3), 
                       sharex=True, sharey=True)
                       
for i, (species, group) in enumerate(iris.groupby('species')): 
    ax[i].scatter(group['petalLength'], group['sepalWidth'],
                  color=color_map[species], alpha=0.3, 
                  edgecolor=None, label=species)
    ax[i].legend(frameon=True, title='species') 
    
plt.xlabel('petalLength')
plt.ylabel('sepalLength')

Problem

We’re mixing the what with the how

Toward a well-motivated Declarative Visualization

Imperative
  • Specify How something should be done.
  • Specification and Execution intertwined.
  • “Put a red circle here, and a blue circle here”.
Declarative
  • Specify What should be done.
  • Separates Specification from Execution.
  • “Map to a position, and to a color".

Declarative visualization lets you think about data and relationships, rather than incidental details.

Declarative visualization in Python

on the Vega and Vega-Lite grammars.

D3 is the go-to vis libary

But working with D3 can be hard