How to use selectors
This guide highlights different selectors that can be used in a dashboard. Selectors do not serve a purpose on their own, but they enable you to change how the input is given to other models, for example, the Filter or the Parameter model.
The Filter or the Parameter model accept the selector argument, where a selector model can be entered to choose how the user should input their choices for the respective models.
Categorical selectors
Within the categorical selectors, a clear distinction exists between multi-option and single-option selectors. For instance, the Checklist functions as a multi-option selector by default while the RadioItem serves as a single-option selector by default. However, the Dropdown can function as both a multi-option or single-option selector.
For more information, refer to the API reference of the selector, or the documentation of its underlying Dash component:
Dropdownbased ondcc.DropdownChecklistbased ondcc.ChecklistRadioItemsbased ondcc.RadioItems
If you have binary data (such as False/True or 0/1), you might prefer to use a dedicated boolean selector instead.
Configuring options
When configuring the options of the categorical selectors, you can either give:
- a list of values
options = ['Value A', 'Value B', 'Value C'] - or a dictionary of label-value mappings
options=[{'label': 'True', 'value': True}, {'label': 'False', 'value': False}]
The later is required if you want to provide different display labels to your option values or in case you want to provide boolean values as options. In this case, you need to provide a string label for your boolean values as boolean values cannot be displayed properly as labels in the underlying Dash components.
Styled dropdowns
You can customize two predefined dropdown styles that can be customized using the variant argument. If no variant is specified, the default style applied is variant="filled".
Styled dropdowns
from vizro import Vizro
import vizro.plotly.express as px
import vizro.models as vm
iris = px.data.iris()
page = vm.Page(
title="Styled dropdown",
components=[
vm.Graph(figure=px.scatter(iris, x="sepal_length", y="petal_width", color="species")),
],
controls=[
vm.Filter(column="species", selector=vm.Dropdown(title="Filled")),
vm.Filter(column="species", selector=vm.Dropdown(variant="plain", title="Plain")),
],
)
dashboard = vm.Dashboard(pages=[page])
Vizro().build(dashboard).run()
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# Still requires a .py to add data to the data manager and parse YAML configuration
# See yaml_version example
pages:
- components:
- figure:
_target_: scatter
data_frame: iris
x: sepal_length
y: petal_width
color: species
type: graph
controls:
- column: species
selector:
type: dropdown
title: Plain
variant: plain
type: filter
- column: species
selector:
type: dropdown
title: Filled
type: filter
title: Styled dropdown
Numerical selectors
For more information, refer to the API reference of the selector, or the documentation of its underlying Dash component:
Sliderbased ondcc.SliderRangeSliderbased ondcc.RangeSlider
Using float values and step with an integer value
When configuring the Slider and the RangeSlider with float values, and using step with an integer value, you may notice unexpected behavior, such as the drag value being outside its indicated marks. To our knowledge, this is a current bug in the underlying dcc.Slider and dcc.RangeSlider component, which you can circumvent by adapting the step size as needed.
Temporal selectors
For more information, refer to the API reference of the selector, or the documentation of its underlying Dash component:
DatePickerbased ondmc.DatePickerInput
Note
When configuring the DatePicker make sure to provide your dates for min, max and value arguments in "yyyy-mm-dd" format or as datetime type (for example, datetime.datetime(2024, 01, 01)).
Boolean selectors
For more information, refer to the API reference of the selector, or the documentation of its underlying Dash component:
Switchbased ondbc.Switch
Hierarchical selectors
For more information, refer to the API reference of the selector, or the documentation of its underlying Dash component:
Cascaderbased onvdc.Cascader
Hierarchical selectors show choices in a nested menu of groups. options gives the structure of a tree of values (the leaves of the tree), for example:
options = {
"Asia": ["Japan", "India"],
"Europe": {"West": ["France", "Germany"], "North": ["Norway"]},
}
By default, value is set according to the first group at the top of the tree:
- If
multi=False, by defaultvalueis the first leaf listed under the first group. Here the first group isAsia, and its first country isJapan, sovalue="Japan". - If
multi=True, by defaultvalueis all leaves listed under the first group. Here the first group isAsia, sovalue=["Japan", "India"].
You can pick a different starting selection by setting value on Cascader.
Hierarchical selector multi vs single
from vizro import Vizro
import vizro.plotly.express as px
import vizro.models as vm
gapminder = px.data.gapminder().query("year == 2007")
options = {
"Asia": ["Japan", "India"],
"Europe": {"West": ["France", "Germany"], "North": ["Norway"]},
}
page = vm.Page(
title="Gapminder 2007",
components=[
vm.Graph(
figure=px.scatter(
gapminder,
x="gdpPercap",
y="lifeExp",
size="pop",
color="continent",
hover_name="country",
)
),
],
controls=[
vm.Filter(column=["continent", "country"], selector=vm.Cascader(options=options)),
vm.Filter(column=["continent", "country"], selector=vm.Cascader(options=options, multi=False, value="France"))
],
)
dashboard = vm.Dashboard(pages=[page])
Vizro().build(dashboard).run()
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# Still requires a .py to add data to the data manager and parse YAML configuration
# See yaml_version example
pages:
- components:
- figure:
_target_: scatter
data_frame: gapminder
x: gdpPercap
y: lifeExp
size: pop
color: continent
hover_name: country
type: graph
controls:
- column:
- continent
- country
type: filter
selector:
type: cascader
options: options
- column:
- continent
- country
type: filter
selector:
type: cascader
options: options
multi: false
value: France
title: Gapminder 2007

Hierarchical selectors can be used in hierarchical filters and parameters.
Add a tooltip
The description argument enables you to add helpful context to your selector by displaying an info icon next to its title. Hovering over the icon shows a tooltip with your provided text.
You can provide Markdown text as a string to use the default info icon or a Tooltip model to use any icon from the Google Material Icons library.
Selectors with tooltip
import vizro.models as vm
import vizro.plotly.express as px
from vizro import Vizro
iris = px.data.iris()
page = vm.Page(
title="Selectors with icons",
components=[
vm.Graph(
figure=px.scatter(iris, x="sepal_length", y="sepal_width")
),
],
controls=[
vm.Filter(
column="species",
selector=vm.Checklist(
title="Select Species",
description="""
Select which species of iris you like.
[Click here](https://en.wikipedia.org/wiki/Iris_flower_data_set)
to learn more about flowers.""",
)
),
]
)
dashboard = vm.Dashboard(pages=[page])
Vizro().build(dashboard).run()
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pages:
- title: Selectors with icons
components:
- type: graph
figure:
_target_: scatter
data_frame: iris
x: sepal_length
y: sepal_width
controls:
- column: species
type: filter
selector:
type: checklist
title: Select Species
description: |
Select which species of iris you like.
[Click here](https://en.wikipedia.org/wiki/Iris_flower_data_set) to learn more about flowers.
The extra argument
Currently each selector is based on an underlying Dash component as mentioned in the sections above. Using the extra argument you can pass extra arguments to the underlying object in order to alter it beyond the chosen defaults. The available arguments can be found in the documentation of each underlying component that was linked in the respective sections above.
Note
Using extra is a quick and flexible way to alter a component beyond what Vizro offers. However, it is not a part of the official Vizro schema and the underlying implementation details may change. If you want to guarantee that your apps keep running, we recommend that you pin your Vizro version.
An example would be to make the RadioItem display inline instead of stacked vertically. For this you can use extra={"inline": True} argument:
Inline Radio Items
import vizro.models as vm
import vizro.plotly.express as px
from vizro import Vizro
iris = px.data.iris()
page = vm.Page(
title="Inline Radio Items",
components=[
vm.Graph(
figure=px.scatter(iris, x="sepal_length", y="sepal_width")
),
],
controls=[
vm.Filter(
column="species",
selector=vm.RadioItems(
title="Select Species",
extra={"inline": True}
)
)
]
)
dashboard = vm.Dashboard(pages=[page])
Vizro().build(dashboard).run()
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