How to extend and customize Vizro dashboards

At its simplest, Vizro enables low-code configuration, but you can also customize it to go beyond its default capabilities by incorporating any Dash component, Dash callback, or Plotly chart.

• Vizro customizations. You can customize Vizro to extend the default functionality of Vizro and create Python functions as customized Plotly charts, tables, dashboard components, actions, or reactive HTML components, and then plug them directly into the existing Vizro dashboard configuration (as explained below).

• Dash customizations. You can add custom Dash callbacks directly to any Vizro dashboard, enabling you to code beneath the Vizro layer and control Dash directly.

• CSS customizations. You can add custom CSS to any Vizro dashboard, enabling users to deviate from the default styling and create a unique look and feel for their dashboard.

• React.js customizations. You can add custom React.js components directly to any Vizro dashboard, enabling users to go beneath both the Vizro and Dash layers and control React.js directly

Vizro offers the ability to combine ease of use of low-code configuration, with the flexibility of a range of high-code extensions to expand your dashboard's functionality.

Vizro customizations

Custom charts

You can create custom chart functions in Vizro by wrapping Plotly chart code inside a Vizro chart function wrapper, and then use the functions directly inside the Vizro dashboard configuration. This enables the creation of plotly.graph_objects charts with multiple traces, or plotly_express charts with post update customizations

Custom tables

If the available arguments for the dash_ag_grid or dash_data_table models are insufficient, you can create a custom Dash AG Grid or Dash DataTable.

Custom components

You can create a custom component based on any dash-compatible component (for example, dash-core-components, dash-bootstrap-components, dash-html-components).

All Vizro's components are based on Dash and ship with a set of defaults that can be modified. If you would like to overwrite one of those defaults, or if you would like to use extra args or kwargs of those components, then this is the correct way to include those. You can use any existing attribute of any underlying Dash component with this method.

Custom actions

If you want to use the Action model to perform functions that are not available in the built-in action functions, you can create your own custom action. Like other actions, custom actions can also be added as an element inside the actions chain, and triggered with one of dashboard components.

Custom figures

Custom figures are useful when you need a component that reacts to filter and parameter controls.

Vizro's Figure model accepts the figure argument, where you can enter any custom figure function as described in the how-to guide for figures.

Use custom functions in yaml/json configuration

Exposing configuration to untrusted users

Exposing Vizro configuration to untrusted users may pose a security risk. A user with access to YAML/JSON configuration can potentially execute arbitrary Python code when the dashboard is run. The security of your yaml/json configuration should be regarded as equivalent to the security of your app.py file.

It is possible to refer to custom functions that are used as CapturedCallable by their import path in a yaml/json configuration of the dashboard.

In the above guides, you will find examples on how the Vizro schema can be extended by using custom Python code. It is possible to refer to these custom functions in the yaml/json configuration by using the _target_ key and the correct import path.

Custom charts with YAML config example

app.pydashboard.yamlcustom_charts.py

from pathlib import Path

import vizro.plotly.express as px
import yaml
from vizro import Vizro
from vizro.managers import data_manager
from vizro.models import Dashboard

data_manager["iris"] = px.data.iris()

dashboard = yaml.safe_load(Path("dashboard.yaml").read_text(encoding="utf-8"))  # (1)!
dashboard = Dashboard(**dashboard)

if __name__ == "__main__":
    Vizro().build(dashboard).run()

1. Parse the YAML or JSON configuration that lies in a separate file.

title: "Dashboard Example"
pages:
- title: "Custom Bar Chart"
    components:
    - type: "graph"
        figure:
        _target_: "custom_charts.custom_bar" # (1)!
        data_frame: "iris"
        x: "sepal_length"
        y: "sepal_width"

1. Here we refer to the import path of the custom chart function. If you define the custom chart in app.py, then use __main__ as the import path. Note that the import path will be interpreted by pydantics ImportString type.

import pandas as pd
import plotly.graph_objects as go
from vizro.models.types import capture


@capture("graph")
def custom_bar(data_frame: pd.DataFrame, x: str, y: str) -> go.Figure:  # (1)!
    """Custom bar chart."""
    return go.Figure(data=[go.Bar(x=data_frame[x], y=data_frame[y])])

1. Definition of the custom chart function as usual.

Validate dashboards without defining CapturedCallable functions

It is possible to validate a dashboard configuration without importing or executing some CapturedCallable functions.

You can use this method when you want to check if the dashboard configuration is valid, but you don't want to import or execute the custom functions until run-time, which may be in a sandboxed environment. This is useful when the custom functions are not available at validation time, or when they originate from untrusted sources (e.g. when a large language model is used to generate that code).

Note

If you validate your dashboard configuration with a CapturedCallable function that is undefined, you will not be able to start the server. Hence when running the dashboard, you will need to recreate a dashboard object with all the CapturedCallable functions defined.

Validating dashboards without executing CapturedCallable functions

import vizro.models as vm
import vizro.plotly.express as px
from vizro import Vizro
from vizro.managers import data_manager

data_manager["iris"] = px.data.iris()

dashboard_config = {
    "title": "Test dashboard",
    "pages": [
        {
            "title": "Page 1",
            "components": [
                {
                    "type": "ag_grid",
                    "figure": {"_target_": "llm_generated_grid", "data_frame": "iris"},
                },
            ],
        }
    ],
}

dashboard = vm.Dashboard.model_validate(
    dashboard_config,
    context={
        "allow_undefined_captured_callable": [
            "llm_generated_grid",
        ]
    },
)
app = Vizro().build(dashboard)  # (1)!
try:
    app.run()  # (2)!
except ValueError as exc:
    print(exc)

1. The dashboard configuration contains a CapturedCallable function that is undefined but allowed by allow_undefined_captured_callable. The app can still be built without raising any errors.
2. However, it is not possible to run the app without undefined CapturedCallables. This raises an error.

Dash customizations

Since Vizro is built using Dash, it is possible to use Dash callbacks directly in any Vizro dashboard. This enables you to code beneath the Vizro layer and control Dash directly, which is especially useful when working with callbacks

Here is an example showing a Dash callback within Vizro, enabling an interaction between data points in a scatter plot and the content of a text card:

Dash callback example

app.py

from dash import callback, Input, Output
import vizro.models as vm
import vizro.plotly.express as px
from vizro import Vizro

@callback(
    Output("card_id", "children"),
    Input("source_chart", "clickData")
)
def update_card(click_data):
    if click_data is None:
        return "Click on the graph to select a data point."
    return f"Clicked species: '{click_data['points'][0]['customdata'][0]}'"

page = vm.Page(
    title="Example: Dash callback within Vizro",
    components=[
        vm.Graph(id="source_chart",
                 figure=px.scatter(px.data.iris(), x="sepal_width", y="sepal_length", color="species", custom_data=["species"])),
        vm.Card(id="card_id",
                text="Click on the graph to apply filter interaction."),
    ]
)

dashboard = vm.Dashboard(pages=[page])
Vizro().build(dashboard).run()

Run and edit this code in Py.Cafe

CSS customizations

Vizro is opinionated about visual formatting, and some elements, such as the layout of the navigation and controls, are fixed. You can customize some settings such as background colors, fonts, and other styles via CSS overrides.

For more information, see our documentation on customizing CSS

React.js customizations

It is possible to create custom React.js components and add them directly to any Vizro dashboard so enabling you to code beneath both the Vizro and Dash layers and control React.js directly

For more information, see the documentation on using React.js components with Dash