Layered & Multi-View Charts#

Along with the basic Chart object, Altair provides a number of compound plot types that can be used to create stacked, layered, faceted, and repeated charts. They are summarized in the following tables:

class

functional form

operator form

reference

LayerChart

alt.layer(chart1, chart2)

chart1 + chart2

Layered Charts

HConcatChart

alt.hconcat(chart1, chart2)

chart1 | chart2

Horizontal Concatenation

VConcatChart

alt.vconcat(chart1, chart2)

chart1 & chart2

Vertical Concatenation

class

method form

reference

RepeatChart

chart.repeat(row, column)

Repeated Charts

FacetChart

chart.facet(facet, row, column)

Faceted Charts

Layered Charts#

Layered charts allow you to overlay two different charts on the same set of axes. They can be useful, for example, when you wish to draw multiple marks for the same data; for example:

import altair as alt
from vega_datasets import data

stocks = data.stocks.url

base = alt.Chart(stocks).encode(
    x='date:T',
    y='price:Q',
    color='symbol:N'
).transform_filter(
    alt.datum.symbol == 'GOOG'
)

base.mark_line() + base.mark_point()

Here we have used the + operator to create a layered chart; alternatively we could use the alt.layer function, which accepts as its arguments any number of charts:

alt.layer(
  base.mark_line(),
  base.mark_point(),
  base.mark_rule()
).interactive()

The output of both of these patterns is a LayerChart object, which has properties and methods similar to the Chart object.

Order of Layers#

In a layered chart, the order of layers is determined from the order in which they are specified. For example, when creating a chart using layer1 + layer2 or alt.layer(layer1, layer2), layer1 will appear below layer2, and layer2 may obscure the marks of layer1.

For example, consider the following chart where we plot points on top of a heat-map:

import altair as alt
from vega_datasets import data

source = data.movies.url

heatmap = alt.Chart(source).mark_rect().encode(
    alt.X('IMDB_Rating:Q').bin(),
    alt.Y('Rotten_Tomatoes_Rating:Q').bin(),
    alt.Color('count()').scale(scheme='greenblue')
)

points = alt.Chart(source).mark_circle(
    color='black',
    size=5,
).encode(
    x='IMDB_Rating:Q',
    y='Rotten_Tomatoes_Rating:Q',
)

heatmap + points

If we put the two layers in the opposite order, the points will be drawn first and will be obscured by the heatmap marks:

points + heatmap

If you do not see the expected output when creating a layered chart, make certain that you are ordering the layers appropriately.

Horizontal Concatenation#

Displaying two plots side-by-side is most generally accomplished with the HConcatChart object, which can be created using the hconcat function or the | operator.

For example, here is a scatter-plot concatenated with a histogram showing the distribution of its points:

import altair as alt
from vega_datasets import data

iris = data.iris.url

chart1 = alt.Chart(iris).mark_point().encode(
    x='petalLength:Q',
    y='petalWidth:Q',
    color='species:N'
).properties(
    height=300,
    width=300
)

chart2 = alt.Chart(iris).mark_bar().encode(
    x='count()',
    y=alt.Y('petalWidth:Q').bin(maxbins=30),
    color='species:N'
).properties(
    height=300,
    width=100
)

chart1 | chart2

This example uses the | operator, but could similarly have been created with the hconcat() function:

alt.hconcat(chart1, chart2)

The output of both of these is an HConcatChart object, which has many of the same top-level methods and attributes as the Chart object.

Finally, keep in mind that for certain types of horizontally-concatenated charts, where each panel modifies just one aspect of the visualization, repeated and faceted charts are more convenient (see Repeated Charts and Faceted Charts for more explanation).

Vertical Concatenation#

Similarly to Horizontal Concatenation above, Altair offers vertical concatenation via the vconcat() function or the & operator.

For example, here we vertically-concatenate two views of the same data, with a brush selection to add interaction:

import altair as alt
from vega_datasets import data

source = data.sp500.url

brush = alt.selection_interval(encodings=['x'])

base = alt.Chart(source).mark_area().encode(
    x = 'date:T',
    y = 'price:Q'
).properties(
    width=600,
    height=200
)

upper = base.encode(alt.X('date:T').scale(domain=brush))

lower = base.properties(
    height=60
).add_params(brush)

alt.vconcat(upper, lower)

Note that we could just as well have used upper & lower rather than the more verbose alt.vconcat(upper, lower).

As with horizontally-concatenated charts, keep in mind that for concatenations where only one data grouping or encoding is changing in each panel, using Repeated Charts or Faceted Charts can be more efficient.

Repeated Charts#

The RepeatChart object provides a convenient interface for a particular type of horizontal or vertical concatenation, in which the only difference between the concatenated panels is modification of one or more encodings.

For example, suppose you would like to create a multi-panel scatter-plot to show different projections of a multi-dimensional dataset. Let’s first create such a chart manually using hconcat and vconcat, before showing how repeat can be used to build the chart more efficiently:

import altair as alt
from vega_datasets import data

iris = data.iris.url

base = alt.Chart().mark_point().encode(
    color='species:N'
).properties(
    width=200,
    height=200
).interactive()

chart = alt.vconcat(data=iris)
for y_encoding in ['petalLength:Q', 'petalWidth:Q']:
    row = alt.hconcat()
    for x_encoding in ['sepalLength:Q', 'sepalWidth:Q']:
        row |= base.encode(x=x_encoding, y=y_encoding)
    chart &= row
chart

In this example, we explicitly loop over different x and y encodings to create a 2 x 2 grid of charts showing different views of the data. The code is straightforward, if a bit verbose.

The RepeatChart pattern, accessible via the Chart.repeat() method, makes this type of chart a bit easier to produce:

import altair as alt
from vega_datasets import data
iris = data.iris.url

alt.Chart(iris).mark_point().encode(
    alt.X(alt.repeat("column"), type='quantitative'),
    alt.Y(alt.repeat("row"), type='quantitative'),
    color='species:N'
).properties(
    width=200,
    height=200
).repeat(
    row=['petalLength', 'petalWidth'],
    column=['sepalLength', 'sepalWidth']
).interactive()

The Chart.repeat() method is the key here: it lets you specify a set of encodings for the row and/or column which can be referred to in the chart’s encoding specification using alt.repeat('row') or alt.repeat('column').

Another option to use the repeat method is for layering. Here below the columns US_Gross and Worldwide_Gross are layered on the y-axis using alt.repeat('layer'):

import altair as alt
from vega_datasets import data

source = data.movies()

alt.Chart(source).mark_line().encode(
    x=alt.X("IMDB_Rating").bin(),
    y=alt.Y(alt.repeat('layer')).aggregate('mean').title("Mean of US and Worldwide Gross"),
    color=alt.ColorDatum(alt.repeat('layer'))
).repeat(layer=["US_Gross", "Worldwide_Gross"])