mckinsey/vizro

Features

• Data Visualization - Integrates charts and KPI cards into multi-page applications to represent structured data through graphical plots.
• Configuration-Based Dashboarding - Builds production-ready data visualization applications using Python configurations instead of frontend code.
• Declarative Application Definitions - Defines multi-page visualization applications using structured configuration files such as YAML, JSON, or Python dictionaries.
• Low-Code Dashboard Frameworks - The product enables the creation of production-ready data visualization applications and prototypes using low-code Python configuration.
• AI Dashboard Generators - Creates interactive charts and dashboard layouts automatically via generative AI and model context protocol integration.
• Visualization Configurations - Generates validated data visualization configurations using large language models and the model context protocol.
• Model Context Protocol Servers - Implements a standardized server that allows LLMs to generate and validate data visualization code.
• Natural Language Visualization Generators - Converts natural language descriptions into interactive charts and structured visual layouts using AI.
• Business Intelligence Tools - Develops multi-page applications with KPI cards and aggregated metrics for business reporting.
• Data Catalogs - Registers external data sources from configuration files to decouple data loading logic from visualization code.
• Data Filtering - Provides mechanisms to filter datasets used by graphs and tables based on user inputs from checklists or sliders.
• Data Source Loading Strategies - Loads data as either static sets for stability or dynamic functions to ensure content is refreshed.
• Chart-Control Bindings - Connects custom charts to filters and parameters for dynamic updates based on user input.
• Dynamic Filtering - Generates data filters that automatically update available options based on the current state of data sources.
• External Data Integrations - Integrates local or remote data sources via file paths or URLs for use in visualizations.
• Parametric Data Loading - Passes dashboard-controlled arguments to data loading functions to filter datasets in real time.
• Property-Based Filtering - Updates target graphs or tables based on specific attributes selected by the user in a source component.
• LLM Dashboard Configuration Generation - Translates natural language descriptions into interactive charts and dashboard configurations using LLMs.
• Interactive Data Exploration Tools - Creates dashboards with cross-filtering, reactive controls, and dynamic data grids for real-time analysis.
• Dataset Filters - The product provides interactive selectors to restrict the data shown across page components.
• Component State Bindings - Binds user input selectors to a central state management system that updates reactive charts and UI components.
• UI Component Configurations - Translates Python dictionaries or YAML files into a structured hierarchy of dashboard pages and components.
• Reactive Visualization Dashboards - Creates reactive graphs and figures that update automatically in response to user control inputs.
• Cross-Component Selection Synchronization - Synchronizes the active selection of data points across different visual components when a user interacts with a graph.
• Cross-Filtering Systems - The product triggers control updates through user interaction with a graph, allowing one component to filter others.
• Dashboard Page Managers - Provides utilities to define multiple dashboard pages with unique URL routing and organized component layouts.
• Data Visualization Libraries - Provides a library for creating reactive dashboards with cross-filtering and dynamic data catalogs.
• Parameter-Driven Component Updates - Provides dynamic updates to chart attributes and component arguments based on user-selectable parameters.
• Parametric UI Updates - Passes user-selected parameters into data-loading functions to dynamically change the retrieved dataset in real time.
• Component Interaction Management - Implements a system to manage interactions where one graph acts as a data source to trigger updates in other components.
• Low-Code UI Orchestrators - Defines multi-page analytical applications via YAML, JSON, or Python without extensive frontend engineering.
• Navigation Configurations - Provides structures for defining sidebars, top bars, and collapsible menus to manage multi-page dashboard navigation.
• Reactive Parameter Bindings - Binds user input selectors to component arguments to dynamically update graph titles or axes.
• Text Display Widgets - Renders plain text and styled call-out cards to present key performance indicators and critical metrics.
• Data App Frameworks - Provides a framework for building data-centric dashboards without requiring advanced frontend engineering or design expertise.
• Python Data Dashboard Frameworks - Provides a low-code framework for building production-ready data visualization applications using Python.
• Reactive Data Binding - Binds data-driven components to user controls so they update automatically when filters change.
• MCP Servers - Implements a model context protocol server to translate natural language prompts into validated dashboard configurations.
• Markdown Integration - Integrates formatted text and cards using Markdown syntax to provide necessary context and documentation for dashboards.
• Interactive Data Grids - Integrates interactive data grids for exploring and analyzing large datasets within the dashboard.
• Data Grids - Uses data grids to provide structured and interactive views of information.
• CSV Exports - Allows downloading the current state of all graphs and tables on a dashboard page as CSV files.
• Data Export - Allows exporting raw data series from visualizations into structured formats like CSV.
• Interactive Table Rendering - Displays dataframes in interactive tables with pre-configured sorting and pagination.
• Generative AI Insights - Produces automated analytical charts and business insights using integrated generative AI capabilities.
• Interactive Action Triggers - Triggers specific functions or status notifications when users interact with buttons or other dashboard components.
• Custom Action Programming - Enables the programming of unique functional behaviors and triggers not provided by the default action library.
• Local Development Servers - Launches dashboards on a local development server for iterative testing via scripts or notebooks.
• Application Cloud Deployments - Supports pushing full dashboard applications to cloud hosting environments for production access.
• Real-Time Data Synchronization - Updates visualizations in real-time when the underlying data source in a connected catalog changes.
• Application Logic Hooks - Provides mechanisms to execute developer-defined functions for specialized application behaviors beyond standard visualization operations.
• Configuration Validation - Ensures application configurations adhere to specific types and rules with clear error messages.
• Custom Action Handlers - Allows developers to define custom Python handlers to extend standard dashboard actions with bespoke behavioral logic.
• Visualization Extenders - Builds custom charts using figure objects and update calls to extend standard plotting capabilities.
• External Component Integration - Integrates custom React components and external framework libraries to build specialized charts and interface elements.
• Model-View-Controller Patterns - Binds user input selectors to data-loading functions and UI components through a central state management system.
• Schema-Based State Validation - Ensures application definitions adhere to strict type rules and structures using predefined data models.
• Action Trigger Components - Implements UI elements that trigger specific Python functions through defined action mappings and input/output flows.
• UI Event Mappings - Routes UI events to custom Python functions that update application state or trigger notifications.
• Application Appearance Customization - Adjusts the visual layout, themes, and colors to change the overall look and feel of the analytical application.
• Branding Customization - Allows application of custom CSS, themes, and corporate logos to align with brand identity guidelines.
• Chart Embeddings - Provides capabilities to integrate charts into dashboards using direct function calls or structured configuration files.
• UI-to-Logic Data Mapping - Connects input components to action functions and routes resulting data back to UI model properties.
• CSS Styling - Supports the application of custom visual styles using cascading stylesheet syntax and third-party Bootstrap themes.
• Custom Component Builders - Provides a framework for creating bespoke interface elements by inheriting from base models and defining build methods.
• Reactive Visual Components - Enables the definition of custom figure functions that return UI components reacting dynamically to dashboard filters.
• Component Subclassing - Supports modifying built-in UI elements by subclassing their models to alter default configurations or rendering logic.
• Dashboard Common Tasks - Ships pre-defined operations for common dashboard needs, including dataset downloads and cross-filtering.
• Advanced Data Components - Integrates advanced grid components that allow for custom figure configurations and complex computations.
• Data Grids - Embeds interactive tabular components for displaying and editing structured data within the UI.
• Reactive KPI Components - Renders key performance indicators that dynamically update their text based on dashboard control inputs.
• Flexbox Layout Models - Utilizes a flexible box model to control the alignment, spacing, and responsiveness of dashboard components.
• Flexible Layout Nesting - Combines grid and flexbox systems to organize components into nested sections and switchable tabs.
• Hierarchical Data Filters - Implements tree-based hierarchical filters that allow users to narrow data selections from broad to specific values.
• Custom Aggregation Figures - Allows the creation of specialized figures using decorated functions that perform dynamic data aggregation.
• Interactive Parameter Definitions - Changes specific arguments of target components based on user selections within a source graph.
• Interactive Table Components - Provides interactive table components supporting dynamic sorting, filtering, and searching of datasets.
• KPI Cards - Provides styled KPI cards for displaying aggregated data metrics and reference comparisons within dashboards.
• Live Preview Renderers - Renders dashboard designs in real-time for iterative adjustments to layout and visual elements.
• Metric Comparison Cards - Renders dynamic cards showing aggregated metrics and delta comparisons against reference values.
• Navigation Menus - Implements navigation menus and icon-based bars to manage movement between different analytical dashboard pages.
• Nested Layout Containers - Organizes components into nested frames and styled sections to create a clear hierarchical dashboard structure.
• Page Layout Frameworks - Provides a system for organizing the high-level structural arrangement of dashboards using consistent containers and grids.
• Panel Groups - Groups related filters and parameters into titled panels to visually organize user input sets.
• Component Styling - Modifies the appearance of specific UI elements through granular component-level configuration arguments.
• Grid Layout Systems - Implements a grid system of defined rows and columns for precise placement of visualizations and controls.
• Tabbed Interfaces - Organizes multiple containers into a tabbed interface to toggle between related analytical content.
• Tabbed Navigation - Organizes related visualization components into switchable tabbed views to separate different analytical perspectives.
• URL State Synchronization - Serializes the current configuration of filters and parameters into the page URL for shareable dashboard states.
• User Interaction Handling - Captures and processes user interaction events to trigger UI updates and execute defined custom functions.
• Visual Style Customization - Allows overriding default visual styles using custom CSS or external stylesheets to modify the overall application appearance.
• Chart Visual Style Customizations - Applies independent predefined light or dark themes to individual data visualization elements.
• Visual Themes - Implements global light and dark visual themes for dashboards and charts with a user-facing toggle.
• Theme Customization - Provides capabilities to personalize the dashboard's appearance through defined color palettes and visual styles.
• Automated EDA and Visualization - Low-code toolkit for building visualization apps.
• Deployment - Toolkit for modular data visualization applications.

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