Jupyter Snippet CB2nd 06_altair

6.6. Creating plots with Altair and the Vega-Lite specification

import altair as alt

alt.list_datasets()

['airports',
 ...
 'driving',
 'flare',
 'flights-10k',
 'flights-20k',
 'flights-2k',
 'flights-3m',
 'flights-5k',
 'flights-airport',
 'gapminder',
 ...
 'wheat',
 'world-110m']

df = alt.load_dataset('flights-5k')

df.head(3)

alt.Chart(df).mark_point().encode(
    x='date',
    y='delay',
    size='distance',
)

df_la = df[df['origin'] == 'LAX']

x = alt.X('date', bin=True)
y = 'average(delay)'

alt.Chart(df_la).mark_bar().encode(
    x=x,
    y=y,
)

sort_delay = alt.SortField(
    'delay', op='average', order='descending')

x = alt.X('origin', sort=sort_delay)
y = 'average(delay)'

alt.Chart(df).mark_bar().encode(
    x=x,
    y=y,
)

{
 "$schema": "https://vega.github.io/schema/vega-lite/v1.2.1.json",
 "data": {
  "values": [
   {
    "date": "2001-01-10 18:20:00",
    "delay": 25,
    "destination": "HOU",
    "distance": 192,
    "origin": "SAT"
   },
   ...
  ]
 },
 "encoding": {
  "x": {
   "field": "origin",
   "sort": {
    "field": "delay",
    "op": "average",
    "order": "descending"
   },
   "type": "nominal"
  },
  "y": {
   "aggregate": "average",
   "field": "delay",
   "type": "quantitative"
  }
 },
 "mark": "bar"
}