Bindings, Selections, Conditions: Making Charts Interactive

One of the unique features of Altair, inherited from Vega-Lite, is a declarative grammar of not just visualization, but interaction. There are three core concepts of this grammar:

• the selection() object which captures interactions from the mouse or through other inputs to effect the chart. Inputs can either be events like mouse clicks or drags. Inputs can also be elements like a drop-down, radio button or slider. Selections can be used alone but if you want to change any element of your chart you will need to connect them to a condition.

• the condition() function takes the selection input and changes an element of the chart based on that input.

• the bind property of a selection which establishes a two-way binding between the selection and an input element of your chart.

Interactive charts can use one or more of these elements to create rich interactivity between the viewer and the data.

Selections: Building Blocks of Interactions

Selections in Altair come in a few flavors, and they can be bound to particular charts or sub-charts in your visualization, then referenced in other parts of the visualization.

Example: Linked-Brush Scatter-Plot

As a motivation, let’s create a simple chart and then add some selections to it. Here is a simple scatter-plot created from the cars dataset:

import altair as alt
from vega_datasets import data

cars = data.cars.url

alt.Chart(cars).mark_point().encode(
    x='Miles_per_Gallon:Q',
    y='Horsepower:Q',
    color='Origin:N'
)

First we’ll create an interval selection using the selection_interval() function:

brush = alt.selection_interval()  # selection of type "interval"

We can now bind this brush to our chart by setting the selection property:

alt.Chart(cars).mark_point().encode(
    x='Miles_per_Gallon:Q',
    y='Horsepower:Q',
    color='Origin:N'
).add_selection(
    brush
)

The result above is a chart that allows you to click and drag to create a selection region, and to move this region once the region is created.

Conditions: Making the chart respond

This is neat, but the selection doesn’t actually do anything yet. To use this selection, we need to reference it in some way within the chart. Here, we will use the condition() function to create a conditional color encoding: we’ll tie the color to the "Origin" column for points in the selection, and set the color to "lightgray" for points outside the selection:

alt.Chart(cars).mark_point().encode(
    x='Miles_per_Gallon:Q',
    y='Horsepower:Q',
    color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
).add_selection(
    brush
)

As you can see, with this simple change, the color of the points responds to the selection.

This approach becomes even more powerful when the selection behavior is tied across multiple views of the data within a compound chart. For example, here we create a chart object using the same code as above, and horizontally concatenate two versions of this chart: one with the x-encoding tied to "Acceleration", and one with the x-encoding tied to "Miles_per_Gallon"

chart = alt.Chart(cars).mark_point().encode(
    y='Horsepower:Q',
    color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
).properties(
    width=250,
    height=250
).add_selection(
    brush
)

chart.encode(x='Acceleration:Q') | chart.encode(x='Miles_per_Gallon:Q')

Because both copies of the chart reference the same selection object, the renderer ties the selections together across panels, leading to a dynamic display that helps you gain insight into the relationships within the dataset.

Each selection type has attributes through which its behavior can be customized; for example we might wish for our brush to be tied only to the "x" encoding to emphasize that feature in the data. We can modify the brush definition, and leave the rest of the code unchanged:

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

chart = alt.Chart(cars).mark_point().encode(
    y='Horsepower:Q',
    color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
).properties(
    width=250,
    height=250
).add_selection(
    brush
)

chart.encode(x='Acceleration:Q') | chart.encode(x='Miles_per_Gallon:Q')

Selection Types: Interval, Single, Multi

With this interesting example under our belt, let’s take a more systematic look at some of the types of selections available in Altair. For simplicity, we’ll use a common chart in all the following examples; a simple heat-map based on the cars dataset. For convenience, let’s write a quick Python function that will take a selection object and create a chart with the color of the chart elements linked to this selection:

def make_example(selector):
    cars = data.cars.url

    return alt.Chart(cars).mark_rect().encode(
        x="Cylinders:O",
        y="Origin:N",
        color=alt.condition(selector, 'count()', alt.value('lightgray'))
    ).properties(
        width=300,
        height=180
    ).add_selection(
        selector
    )

Next we’ll use this function to demonstrate the properties of various selections.

Interval Selections

An interval selection allows you to select chart elements by clicking and dragging. You can create such a selection using the selection_interval() function:

interval = alt.selection_interval()
make_example(interval)

As you click and drag on the plot, you’ll find that your mouse creates a box that can be subsequently moved to change the selection.

The selection_interval() function takes a few additional arguments; for example we can bind the interval to only the x-axis, and set it such that the empty selection contains none of the points:

interval_x = alt.selection_interval(encodings=['x'], empty='none')
make_example(interval_x)

A special case of an interval selection is when the interval is bound to the chart scales; this is how Altair plots can be made interactive:

scales = alt.selection_interval(bind='scales')

alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N'
).add_selection(
    scales
)

Because this is such a common pattern, Altair provides the Chart.interactive() method which creates such a selection more concisely.

Single Selections

A single selection allows you to select a single chart element at a time using mouse actions. By default, points are selected on click:

single = alt.selection_single()
make_example(single)

By changing some arguments, we can select points on mouseover rather than on click. We can also set the nearest flag to True so that the nearest point is highlighted:

single_nearest = alt.selection_single(on='mouseover', nearest=True)
make_example(single_nearest)

Multiple Selections

A multi selection is similar to a single selection, but it allows for multiple chart objects to be selected at once. By default, chart elements can be added to and removed from the selection by clicking on them while holding the shift key:

multi = alt.selection_multi()
make_example(multi)

In addition to the options seen in selection_single(), the multi selection accepts the toggle parameter, which controls whether points can be removed from the selection once they are added.

For example, here is a plot where you can “paint” the chart objects by hovering over them with your mouse:

multi_mouseover = alt.selection_multi(on='mouseover', toggle=False, empty='none')
make_example(multi_mouseover)

Composing Multiple Selections

Altair also supports combining multiple selections using the &, | and ~ for respectively AND, OR and NOT logical composition operands.

In the following example there are two people who can make an interval selection in the chart. The person Alex makes a selection box when the alt-key (macOS: option-key) is selected and Morgan can make a selection box when the shift-key is selected. We use the alt.Brushconfig() to give the selection box of Morgan a different style. Now, we color the rectangles when they fall within Alex’s or Morgan’s selection.

alex = alt.selection_interval(
    on="[mousedown[event.altKey], mouseup] > mousemove",
    name='alex'
)
morgan = alt.selection_interval(
    on="[mousedown[event.shiftKey], mouseup] > mousemove",
    mark=alt.BrushConfig(fill="#fdbb84", fillOpacity=0.5, stroke="#e34a33"),
    name='morgan'
)

alt.Chart(cars).mark_rect().encode(
    x='Cylinders:O',
    y='Origin:O',
    color=alt.condition(alex | morgan, 'count()', alt.ColorValue("grey"))
).add_selection(
    alex, morgan
).properties(
    width=300,
    height=180
)

With these operators, selections can be combined in arbitrary ways:

• ~(alex & morgan): to select the rectangles that fall outside Alex’s and Morgan’s selections.

• alex | ~morgan: to select the rectangles that fall within Alex’s selection or outside the selection of Morgan

Selection Targets: Fields and Encodings

For any but the simplest selections, the user needs to think about exactly what is targeted by the selection, and this can be controlled with either the fields or encodings arguments. These control what data properties are used to determine which points are part of the selection.

For example, here we create a small chart that acts as an interactive legend, by targeting the Origin field using fields=['Origin']. Clicking on points in the upper-left plot (the legend) will propagate a selection for all points with a matching Origin.

selection = alt.selection_multi(fields=['Origin'])
color = alt.condition(selection,
                      alt.Color('Origin:N', legend=None),
                      alt.value('lightgray'))

scatter = alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color=color,
    tooltip='Name:N'
)

legend = alt.Chart(cars).mark_point().encode(
    y=alt.Y('Origin:N', axis=alt.Axis(orient='right')),
    color=color
).add_selection(
    selection
)

scatter | legend

The above could be equivalently replace fields=['Origin'] with encodings=['color'], because in this case the chart maps color to 'Origin'.

Similarly, we can specify multiple fields and/or encodings that must be matched in order for a datum to be included in a selection. For example, we could modify the above chart to create a two-dimensional clickable legend that will select points by both Origin and number of cylinders:

selection = alt.selection_multi(fields=['Origin', 'Cylinders'])
color = alt.condition(selection,
                      alt.Color('Origin:N', legend=None),
                      alt.value('lightgray'))

scatter = alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color=color,
    tooltip='Name:N'
)

legend = alt.Chart(cars).mark_rect().encode(
    y=alt.Y('Origin:N', axis=alt.Axis(orient='right')),
    x='Cylinders:O',
    color=color
).add_selection(
    selection
)

scatter | legend

By fine-tuning the behavior of selections in this way, they can be used to create a wide variety of linked interactive chart types.

Binding: Adding Data Driven Inputs

With an understanding of the selection types and conditions, you can now add data-driven input elements to the charts using the bind option. As specified by Vega-lite binding, selections can be bound two-ways:

1. Single selections can be bound directly to an input elements. For example, a radio button.

2. Interval selections which can be bound to scale. for example, zooming in on a map.

Input Element Binding

With single selections, an input element can be added to the chart to establish a binding between the input and the selection.

For instance, using our example from above a dropdown can be used to highlight cars from a specific origin :

input_dropdown = alt.binding_select(options=['Europe','Japan','USA'])
selection = alt.selection_single(fields=['Origin'], bind=input_dropdown, name='Country of ')
color = alt.condition(selection,
                    alt.Color('Origin:N', legend=None),
                    alt.value('lightgray'))

alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color=color,
    tooltip='Name:N'
).add_selection(
    selection
)

The above example shows all three elements at work. The :input_dropdown: is :bind: to the :selection: which is called from the :condition: encoded through the data.

The following are the input elements supported in vega-lite:

Input Element	Description	Example
binding_checkbox	Renders as checkboxes allowing for multiple selections of items.	Multiple Interactions
binding_radio	Radio buttons that force only a single selection	Multiple Interactions
binding_select	Drop down box for selecting a single item from a list	Multiple Interactions
binding_range	Shown as a slider to allow for selection along a scale.	US Population Over Time

Bindings and input elements can also be used to filter data on the client side. Reducing noise in the chart and allowing the user to see just certain selected elements:

input_dropdown = alt.binding_select(options=['Europe','Japan','USA'])
selection = alt.selection_single(fields=['Origin'], bind=input_dropdown, name='Country of ')
color = alt.condition(selection,
                    alt.Color('Origin:N', legend=None),
                    alt.value('lightgray'))

alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N',
    tooltip='Name:N'
).add_selection(
    selection
).transform_filter(
    selection
)

Scale Binding

With interval selections, the bind property can be set to the value of "scales". In these cases, the binding will automatically respond to the panning and zooming along the chart:

selection = alt.selection_interval(bind='scales')

alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N',
    tooltip='Name:N'
).add_selection(
    selection
)

Selection Values in Expressions

Selection values can be accessed directly and used in expressions that affect the chart. For example, here we create a slider to choose a cutoff value, and color points based on whether they are smaller or larger than the value:

import altair as alt
import pandas as pd
import numpy as np

rand = np.random.RandomState(42)

df = pd.DataFrame({
    'xval': range(100),
    'yval': rand.randn(100).cumsum()
})

slider = alt.binding_range(min=0, max=100, step=1, name='cutoff:')
selector = alt.selection_single(name="SelectorName", fields=['cutoff'],
                                bind=slider, init={'cutoff': 50})

alt.Chart(df).mark_point().encode(
    x='xval',
    y='yval',
    color=alt.condition(
        alt.datum.xval < selector.cutoff,
        alt.value('red'), alt.value('blue')
    )
).add_selection(
    selector
)

Selector values can be similarly used anywhere that expressions are valid, for example, in a Calculate Transform or a Filter Transform.

Further Examples

Now that you understand the basics of Altair selections and bindings, you might wish to look through the Interactive Charts section of the example gallery for ideas about how they can be applied to more interesting charts.

For more information on how to fine-tune selections, including specifying other mouse and keystroke options, see the Vega-Lite Selection documentation.