Los Clásicos
Aquí tenemos unos ejemplos de gráficos comunes y el código correspondiente en altair
Gráfico de dispersión (aka scatterplot)
import altair as alt
from vega_datasets import data
iris = data.iris()
alt.Chart(iris).mark_point().encode(
x='petalWidth',
y='petalLength',
color='species',
tooltip='species'
)
Gráfico de barras (aka bar chart)
import altair as alt
import pandas as pd
data = pd.DataFrame({
'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})
alt.Chart(data).mark_bar().encode(
x='a',
y='b'
).properties(
height = 400,
width = 500,
)
Mapa de calor (aka heatmap)
import altair as alt
import numpy as np
import pandas as pd
# Compute x^2 + y^2 across a 2D grid
x, y = np.meshgrid(range(-5, 5), range(-5, 5))
z = x ** 2 + y ** 2
# Convert this grid to columnar data expected by Altair
data = pd.DataFrame({'x': x.ravel(),
'y': y.ravel(),
'z': z.ravel()})
alt.Chart(data).mark_rect().encode(
x='x:O',
y='y:O',
color='z:Q'
).properties(
height = 500,
width = 500
)
Histograma
import altair as alt
from vega_datasets import data
movies = data.movies.url
alt.Chart(movies).mark_bar().encode(
alt.X("IMDB_Rating:Q", bin=True),
y='count()',
).properties(
width = 500,
height = 300,
)
Mapa
import altair as alt
from vega_datasets import data
counties = alt.topo_feature(data.us_10m.url, 'counties')
unemp_data = data.unemployment.url
alt.Chart(counties).mark_geoshape().encode(
color='rate:Q'
).transform_lookup(
lookup='id',
from_=alt.LookupData(unemp_data, 'id', ['rate'])
).project(
type='albersUsa'
).properties(
width=500,
height=300
)