Parameters, Conditions, & Filters#

Parameters#

Parameters are the building blocks of interaction in Altair. There are two types of parameters: variables and selections. We introduce these concepts through a series of examples.

Note

This material was changed considerably with the release of Altair 5.

Variables: Reusing Values#

Variable parameters allow for a value to be defined once and then reused throughout the rest of the chart. Here is a simple scatter-plot created from the cars dataset:

import altair as alt
from altair.datasets import data

cars = data.cars.url

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

Variable parameters are created using the param() function. Here, we create a parameter with a default value of 0.1 using the value property:

op_var = alt.param(value=0.1)

In order to use this variable in the chart specification, we explicitly add it to the chart using the add_params() method, and we can then reference the variable within the chart specification. Here we set the opacity using our op_var parameter. The add_params() method adds the parameter to the chart, making it available for use in encodings, conditions, and filters.

op_var = alt.param(value=0.1)

alt.Chart(cars).mark_circle(opacity=op_var).encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N'
).add_params(
    op_var
)

It’s reasonable to ask whether all this effort is necessary. Here is a more natural way to accomplish the same thing that avoids the use of both param() and add_params.

op_var2 = 0.1

alt.Chart(cars).mark_circle(opacity=op_var2).encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N'
)

The benefit of using param() doesn’t become apparent until we incorporate an additional component. In the following example we use the bind property of the parameter, so that the parameter becomes bound to an input element. In this example, that input element is a slider widget.

slider = alt.binding_range(min=0, max=1, step=0.05, name='opacity:')
op_var = alt.param(value=0.1, bind=slider)

alt.Chart(cars).mark_circle(opacity=op_var).encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N'
).add_params(
    op_var
)

Now we can dynamically change the opacity of the points in our chart using the slider. You will learn much more about binding parameters to input elements such as widgets in the section Bindings & Widgets.

Note

A noteworthy aspect of Altair’s interactivity is that these effects are controlled entirely within the web browser. This means that you can save charts as HTML files and share them with your colleagues who can access the interactivity via their browser without the need to install Python.

Selections: Capturing Chart Interactions#

Selection parameters define data queries that are driven by interactive manipulation of the chart by the user (e.g., via mouse clicks or drags). There are two types of selections: selection_interval() and selection_point().

Here we will create a simple chart and then add an selection interval to it. We could create a selection interval via param(select="interval"), but it is more convenient to use the shorter selection_interval.

Here is a simple scatter-plot created from the cars dataset:

import altair as alt
from altair.datasets import data

cars = data.cars.url

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

First we’ll create an interval selection using the selection_interval() function (an interval selection is also referred to as a “brush”):

brush = alt.selection_interval()

We can now add this selection interval to our chart via add_params:

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

So far this example is very similar to what we did in the variable example: we created a selection parameter using brush = alt.selection_interval(), and we attached that parameter to the chart using add_params. One difference is that here we have not defined how the chart should respond to the selection; you will learn this in the next section.

Conditions#

Note

This material was changed considerably with the release of Altair 5.5.0. when() was introduced in 5.4.0 and should be preferred over condition().

The example above is neat, but the selection interval doesn’t actually do anything yet. To make the chart respond to this selection, we need to reference brush within the chart specification. Here, we will use the when() function to create a conditional color encoding:

conditional = alt.when(brush).then("Origin:N").otherwise(alt.value("lightgray"))

alt.Chart(cars).mark_point().encode(
    x="Horsepower:Q",
    y="Miles_per_Gallon:Q",
    color=conditional,
).add_params(
    brush
)

As you can see, the color of the points now changes depending on whether they are inside or outside the selection. Above we are using the selection parameter brush as a predicate (something that evaluates as True or False).

This is controlled by our definition conditional:

conditional = alt.when(brush).then("Origin:N").otherwise(alt.value("lightgray"))

Data points which fall within the selection evaluate as True, and data points which fall outside the selection evaluate to False. The "Origin:N" specifies how to color the points which fall within the selection, and the alt.value('lightgray') specifies that the outside points should be given a constant color value.

Understanding `when()`#

The when-then-otherwise syntax was directly inspired by polars.when, and is similar to an if-else statement written in Python:

# alt.when(brush)
if brush:
    # .then("Origin:N")
    color = "Origin:N"
else:
    # .otherwise(alt.value("lightgray"))
    color = alt.value("lightgray")

Omitting the .otherwise() clause will use the channel default instead:

source = data.cars()
brush = alt.selection_interval()

points = alt.Chart(source).mark_point().encode(
    x="Horsepower",
    y="Miles_per_Gallon",
    color=alt.when(brush).then(alt.value("goldenrod"))
).add_params(
    brush
)

points

Multiple conditional branches (if, elif, ..., elif in Python) are expressed via chained calls to when(). You will see an example with working code in Conditional branches when you have learned about different selection types.

More advanced use of conditions can be found in the when() API reference and in these gallery examples:

Linking Conditions Across Charts#

Conditional encodings become 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 using the same code as above, and horizontally concatenate two versions of this chart: one with the x-encoding tied to "Horsepower", and one with the x-encoding tied to "Acceleration"

chart = alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color=alt.when(brush).then("Origin:N").otherwise(alt.value("lightgray")),
).properties(
    width=250,
    height=250
).add_params(
    brush
)

chart | chart.encode(x='Acceleration: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(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color=alt.when(brush).then("Origin:N").otherwise(alt.value("lightgray")),
).properties(
    width=250,
    height=250
).add_params(
    brush
)

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

As you might have noticed, the selected points are sometimes obscured by some of the unselected points. To bring the selected points to the foreground, we can change the order in which they are laid out via the following conditional order encoding:

# Lower numbers for `order` means further into the background
selected_on_top = alt.when(brush).then(alt.value(1)).otherwise(alt.value(0))
chart.encode(order=selected_on_top) | chart.encode(order=selected_on_top, x='Acceleration:Q')

You can see another example of this in the Selection zorder gallery example.

Filters#

Using a selection parameter to filter data works in much the same way as using it within when(). For example, in transform_filter(brush), we are again using the selection parameter brush as a predicate. Data points which evaluate to True (i.e., data points which lie within the selection) are kept, and data points which evaluate to False are filtered out.

It is not possible to both select and filter in the same chart, so typically this functionality will be used when at least two sub-charts are present. In the following example, we attach the selection parameter to the upper chart, and then filter data in the lower chart based on the selection in the upper chart. You can explore how the counts change in the bar chart depending on the size and position of the selection in the scatter plot.

brush = alt.selection_interval()

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

bars = alt.Chart(cars).mark_bar().encode(
    x='count()',
    y='Origin:N',
    color='Origin:N'
).transform_filter(
    brush
)

points & bars

Selection Types#

Now that we have seen the basics of how we can use a selection to interact with a chart, let’s take a more systematic look at the types of selection parameters available in Altair. There are two main types of selections you’ll work with through mouse actions:

Interval selections: Select ranges of data by clicking and dragging
Point selections: Select individual data points by clicking or hovering

To demonstrate these selection types clearly, we’ll use a consistent visualization approach. The following helper function creates a heatmap from the cars dataset where the color encoding responds to our selection:

def make_example(selector: alt.Parameter) -> alt.Chart:
    cars = data.cars.url

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

Interval Selections#

An interval selection allows you to select a range of data elements by clicking and dragging. This is often referred to as a “brush” selection and is commonly used for filtering, zooming, or highlighting ranges of data points.

Basic Interval Selection#

The simplest interval selection is created with the selection_interval() function:

interval = alt.selection_interval()
make_example(interval)

As you click and drag on the plot, your mouse creates a selection region that can be moved to change which data points are selected.

Customizing Interval Behavior#

The selection_interval() function accepts several arguments for customization. For example, you can limit the selection to only the x-axis and set it so that an empty selection contains no points:

interval_x = alt.selection_interval(encodings=['x'], empty=False)
make_example(interval_x)

The empty=False argument can also be set inside when() to control how individual conditions respond to empty selections. By default, empty=True, which means the selection predicate evaluates to true for all data points when the selection is empty. With empty=False, the predicate evaluates to false for all points when the selection is empty. This affects what happens when no data points are selected yet:

brush = alt.selection_interval()

# Default behavior (empty=True): When no selection is made in this example, all points will be colored according to their "Origin" category (they are considered as *selected*, and colored according to the `then` clause)
color=alt.when(brush).then("Origin:N").otherwise(alt.value("lightgray"))

# Custom behavior (empty=False): When no selection is made in this example, all points will be lightgray (they are considered as *unselected*, and colored according to the `otherwise` clause )
color=alt.when(brush, empty=False).then("Origin:N").otherwise(alt.value("lightgray"))

Setting Initial Values#

When creating interactive visualizations, you often want to guide users by starting with a pre-selected region rather than an empty selection. You can do this using the value parameter of selection_interval().

Setting initial values is particularly useful when:

Creating a filtered overview-detail pattern where a detailed subset is shown
Guiding viewer attention to a particular area of interest immediately
Preserving selection states across different visualizations or user sessions

Here’s an example using time series data where we initially focus on a specific date range containing the 2008 financial crisis:

import altair as alt
from altair.datasets import data
import datetime as dt

source = data.sp500.url

# Define initial date range to select
date_range = (dt.date(2007, 6, 30), dt.date(2009, 6, 30))

# Create interval selection with initial value
brush = alt.selection_interval(
    encodings=['x'],
    value={'x': date_range}
)

# Create base chart for both panels
base = alt.Chart(source, width=600, height=200).mark_area().encode(
    x = 'date:T',
    y = 'price:Q'
)

# Upper panel shows detailed view filtered by the brush
upper = base.encode(
    alt.X('date:T').scale(domain=brush)
)

# Lower panel shows overview with the brush control
lower = base.properties(
    height=60
).add_params(brush)

# Combine the two charts
upper & lower

This example demonstrates an “overview+detail” pattern - a common visualization technique where one chart shows the complete dataset while another shows a detailed view of the selected portion.

When working with datetime values, you can use Python’s native datetime.date or datetime.datetime objects directly. Altair automatically handles the conversion of these objects to the appropriate format for Vega-Lite.

The format of the value parameter depends on the encodings used in the selection:

For selections with x encoding: value={'x': [min, max]}
For selections with y encoding: value={'y': [min, max]}
For selections with both: value={'x': [xmin, xmax], 'y': [ymin, ymax]}

You can also set initial values of a selection with categorical data. In this case the list/tuple specifies the exact categories to be selection rather than a range of categories. For example, to initially select only cars with 4 or 6 cylinders:

cylinder_select = alt.selection_interval(
    encodings=['x'],
    value={"x": [4, 6]}  # Select 4 and 6 (but not 5) cylinder cars
)
make_example(cylinder_select)

Point Selections#

A point selection allows you to select individual data elements one at a time. Unlike interval selections which select ranges, point selections work with discrete data points, making them ideal for interactive legends, tooltips, and highlighting specific data points.

Basic Point Selection#

The simplest point selection is created with the selection_point() function. By default, points are selected on click:

point = alt.selection_point()
make_example(point)

By changing the on parameter to ‘pointerover’, we can select points when hovering over them rather than on click. The on parameter accepts any “Event Stream Selector” supported by Vega and you can see more elaborate example at the end of the Parameter Composition section. We can also set the nearest flag to True so that the nearest point is highlighted instead of when the pointer is directly touching a point:

point_nearest = alt.selection_point(on='pointerover', nearest=True)
make_example(point_nearest)

Point selections also support multi-selection. By default, you can add or remove data points from your selection by holding the shift key while clicking. Try this behavior in the examples above to see how it works.

Setting Initial Values#

You can also set initial values for point selections using the value parameter. For point selections, the value is a dictionary specifying the initial selection based on fields or encodings.

point_initial = alt.selection_point(
    fields=['Origin'],
    value=[{'Origin': 'USA'}]  # Initially select USA
)
make_example(point_initial)

Selection Targets#

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-right plot (the legend) will propagate a selection for all points with a matching Origin.

selection = alt.selection_point(fields=['Origin'])
color = (
    alt.when(selection)
    .then(alt.Color("Origin:N").legend(None))
    .otherwise(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(
    alt.Y('Origin:N').axis(orient='right'),
    color=color
).add_params(
    selection
)

scatter | legend

Alternatively, we could express fields=['Origin'] as encodings=['color'], because our chart maps color to 'Origin'. Also note that there is a shortcut to create interactive legends in Altair described in the section Legend Binding.

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_point(fields=['Origin', 'Cylinders'])
color = (
    alt.when(selection)
    .then(alt.Color("Origin:N").legend(None))
    .otherwise(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(
    alt.Y('Origin:N').axis(orient='right'),
    x='Cylinders:O',
    color=color
).add_params(
    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.

Combining Parameters#

Multiple parameters can be combined in a single chart, either via multiple separate response conditions, different conditional branches in when(), or parameter composition.

Multiple conditions#

In this example, points that are hovered with the pointer will increase in size and those that are clicked will be filled in with red. The empty=False is to ensure that no points are selected to start. Try holding shift to select multiple points on either hover or click.

click = alt.selection_point(empty=False)
hover = alt.selection_point(on='pointerover', empty=False)

points = alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    fill=alt.when(click).then(alt.value('red')),
    size=alt.when(hover).then(alt.value(1000))
).add_params(
    click, hover
)

points

Conditional branches#

when() allows the use of multiple then (elif) branches which can change the behavior of a single encoding in response to multiple different parameters. Here, we fill hovered points in yellow, before changing the fill to red when a point is clicked. Since the mouse is hovering over points while clicking them, both conditions will be active and the earlier branch takes precedence (you can try by changing the order of the two when.then clauses and observing that the points will not change to red when clicked).

click = alt.selection_point(empty=False)
hover = alt.selection_point(on='pointerover', empty=False)

points = alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    fill=(
        alt.when(click)
        .then(alt.value('red'))
        .when(hover)
        .then(alt.value('gold'))
    ),
    size=alt.when(hover).then(alt.value(1000))
).add_params(
    click, hover
)

points

Parameter Composition#

Altair also supports combining multiple parameters using the &, | and ~ for respectively AND, OR and NOT logical composition operands. These parameter compositions can be used with both filters and conditions in Altair. In the following example, only the points that fall within the interval selections of both the scatter plots will be counted in the bar chart (so you will need to make a selection in both charts before the bars shows up).

# empty=False ensure that no points are selected before a selection is drawn
brush = alt.selection_interval(empty=False)
brush2 = alt.selection_interval(empty=False)

points = alt.Chart(cars).mark_point(size=10).encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N'
)

points2 = points.encode(
    x='Acceleration:Q',
    y='Miles_per_Gallon:Q',
)

bars = alt.Chart(cars).mark_bar().encode(
    x='count()',
    y='Origin:N',
    color='Origin:N'
).transform_filter(
    brush & brush2
)

(points.add_params(brush) | points2.add_params(brush2)) & bars

To illustrate how a more complex parameter composition can be applied to a conditional encoding, we can return to our heatmap example. Let’s construct a scenario where there are two people who can make an interval selection in the same 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. Now, we color the chart marks when they fall within Alex’s or Morgan’s selection, but not both’s (note that you need to create both selections before seeing the effect). Here, we also use We use BrushConfig to give the selection box of Morgan a different style to be able to tell them apart.

alex = alt.selection_interval(
    on="[pointerdown[event.altKey], pointerup] > pointermove",
    empty=False,
)
morgan = alt.selection_interval(
    on="[pointerdown[event.shiftKey], pointerup] > pointermove",
    mark=alt.BrushConfig(fill="#fdbb84", fillOpacity=0.5, stroke="#e34a33"),
    empty=False,
)

exlusive_or = (alex | morgan) & ~(alex & morgan)

alt.Chart(cars).mark_rect().encode(
    x='Cylinders:O',
    y='Origin:O',
    color=alt.when(exlusive_or).then("count()").otherwise(alt.value("grey")),
).add_params(
    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 inside either Alex’s or Morgans’ selection.
~(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

For more information on how to fine-tune selections, including specifying other mouse and keystroke options, see the Vega-Lite Selection documentation. Also see BrushConfig for information on how to customize the appearance of the brush.

Parameters, Conditions, & Filters#

Parameters#

Variables: Reusing Values#

Selections: Capturing Chart Interactions#

Conditions#

Understanding when()#

Linking Conditions Across Charts#

Filters#

Selection Types#

Interval Selections#

Basic Interval Selection#

Customizing Interval Behavior#

Setting Initial Values#

Point Selections#

Basic Point Selection#

Setting Initial Values#

Selection Targets#

Combining Parameters#

Multiple conditions#

Conditional branches#

Parameter Composition#

Understanding `when()`#