{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-panel","slug":"pypi-panel","name":"panel","type":"framework","url":"https://pypi.org/project/panel/","page_url":"https://unfragile.ai/pypi-panel","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-panel__cap_0","uri":"capability://automation.workflow.param.based.reactive.parameter.binding.with.automatic.ui.synchronization","name":"param-based reactive parameter binding with automatic ui synchronization","description":"Panel implements a reactive programming model built on top of param.Parameterized, where changes to parameter objects automatically trigger UI updates through bidirectional communication. The framework uses pn.bind() to establish dependencies between Python parameters and frontend widgets, with automatic dependency tracking that ensures efficient updates without manual event handling. This is achieved through Bokeh model synchronization where parameter changes propagate to the browser and frontend events flow back to Python models.","intents":["Build interactive dashboards where UI components automatically reflect data model changes","Create parameter-driven applications that work identically in notebooks, web apps, and batch contexts","Establish bidirectional data flow between Python objects and web interfaces without explicit event listeners"],"best_for":["Data scientists building exploratory tools with live parameter updates","Teams building deployment-agnostic applications using param.Parameterized base classes","Developers needing reactive UIs without managing event handlers manually"],"limitations":["Dependency tracking overhead increases with complex parameter graphs — large applications may experience latency in parameter propagation","Requires all interactive state to be declared as param.Parameter objects; imperative state management is not reactive","Bidirectional binding can create circular update loops if not carefully designed"],"requires":["Python 3.8+","param library (included as dependency)","Bokeh backend for browser synchronization"],"input_types":["param.Parameterized objects","Python callables/functions","Parameter value changes from UI"],"output_types":["Bokeh model updates","Browser DOM updates","Python callback executions"],"categories":["automation-workflow","reactive-programming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_1","uri":"capability://image.visual.multi.library.visualization.pane.system.with.automatic.type.detection.and.rendering","name":"multi-library visualization pane system with automatic type detection and rendering","description":"Panel's pane system provides automatic object-type detection and rendering for 20+ visualization libraries including Matplotlib, Plotly, Bokeh, Altair, Folium, PyVista, and ipywidgets. When a visualization object is passed to Panel, the framework inspects its type and routes it to the appropriate pane class (e.g., panel.pane.Matplotlib, panel.pane.Plotly) which handles conversion to Bokeh models for browser rendering. This eliminates boilerplate conversion code and allows developers to mix visualization libraries seamlessly in a single dashboard.","intents":["Display Matplotlib, Plotly, Bokeh, and Altair plots in the same dashboard without manual conversion","Embed geospatial visualizations (Folium, Deck.gl) and 3D models (PyVista/VTK) in web applications","Reuse existing visualization code without refactoring for web deployment"],"best_for":["Data scientists with existing visualization code in multiple libraries","Teams migrating Jupyter notebooks to web dashboards","Developers building multi-modal dashboards combining maps, 3D, and statistical plots"],"limitations":["Type detection is based on object class inspection; custom visualization classes may not auto-detect","Rendering performance depends on underlying library — Matplotlib plots are rasterized which limits interactivity","Some libraries (e.g., Plotly) require additional dependencies; Panel doesn't bundle all visualization libraries"],"requires":["Python 3.8+","Target visualization library (matplotlib, plotly, bokeh, altair, folium, pydeck, pyvista, etc.)","Bokeh for browser rendering"],"input_types":["matplotlib.figure.Figure","plotly.graph_objects.Figure","bokeh.models.Model","altair.Chart","folium.Map","pydeck.Deck","pyvista.Plotter","ipywidgets.Widget"],"output_types":["Bokeh models","HTML/Canvas rendering","Interactive browser visualizations"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_10","uri":"capability://automation.workflow.integration.with.jupyter.notebooks.and.ipython.display.system","name":"integration with jupyter notebooks and ipython display system","description":"Panel applications render inline in Jupyter notebooks using IPython's display system, enabling interactive dashboards in notebook cells without external servers. The framework detects the notebook environment and uses Jupyter's comm protocol for bidirectional communication between Python and JavaScript. Developers can mix Panel components with notebook cells, creating hybrid notebooks that combine code, visualizations, and interactive controls. The same code renders in notebooks and web servers without modification.","intents":["Build interactive dashboards directly in Jupyter notebooks","Create hybrid notebooks mixing code cells with interactive Panel components","Share reproducible analyses with embedded interactive controls"],"best_for":["Data scientists working in Jupyter notebooks","Teams sharing interactive analyses through notebook files","Researchers creating reproducible computational notebooks"],"limitations":["Notebook rendering uses Jupyter's comm protocol which may not support all Bokeh features (e.g., server-side callbacks)","Large dashboards in notebooks can slow down notebook performance and increase file size","Notebook state is lost on kernel restart; no built-in persistence"],"requires":["Python 3.8+","Jupyter notebook or JupyterLab","ipykernel for comm protocol support"],"input_types":["Panel components in notebook cells","Python code in notebook cells"],"output_types":["Rendered HTML in notebook output cells","Interactive widgets in notebook","Jupyter comm messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_11","uri":"capability://data.processing.analysis.data.table.widget.with.pandas.polars.support.and.built.in.sorting.filtering","name":"data table widget with pandas/polars support and built-in sorting/filtering","description":"Panel's DataFrameViewer and DataFrame widgets provide interactive table rendering for Pandas and Polars DataFrames with built-in sorting, filtering, pagination, and column selection. The widgets are implemented as Bokeh ColumnDataSource models that efficiently handle large datasets through server-side pagination. Users can click column headers to sort, use filter inputs to search, and select rows for further analysis. The selected rows are accessible as a parameter that can be used in downstream computations.","intents":["Display and interact with Pandas/Polars DataFrames in dashboards","Enable users to sort, filter, and select rows without code","Create data exploration interfaces with table-based workflows"],"best_for":["Data scientists building data exploration dashboards","Teams creating data entry and review interfaces","Developers needing interactive table widgets without JavaScript"],"limitations":["Performance degrades with >10,000 rows; requires server-side pagination for larger datasets","Column formatting options are limited (no custom cell renderers); complex formatting requires custom components","Editing cells requires explicit configuration; not enabled by default"],"requires":["Python 3.8+","pandas or polars library","Bokeh for rendering"],"input_types":["pandas.DataFrame","polars.DataFrame","Configuration parameters (sortable, filterable, selectable, etc.)"],"output_types":["Rendered HTML table","Selected rows (as DataFrame)","Sort/filter state"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_12","uri":"capability://automation.workflow.template.system.with.pre.built.bootstrap.and.material.design.layouts","name":"template system with pre-built bootstrap and material design layouts","description":"Panel includes pre-built templates (Bootstrap, Material Design, Fast) that provide consistent styling, navigation, and page structure without CSS knowledge. Templates are Python classes that inherit from BaseTemplate and define header, sidebar, and main content areas. Developers populate template areas with Panel components, and the template handles responsive layout, navigation, and theming. Templates compile to Bokeh models and render as styled HTML in the browser, providing production-ready UI without design overhead.","intents":["Create professional-looking dashboards with consistent styling","Build multi-page applications with navigation","Deploy dashboards without CSS or design expertise"],"best_for":["Data scientists building internal tools","Teams prototyping dashboards quickly","Organizations avoiding design overhead"],"limitations":["Template customization beyond provided options requires CSS knowledge","Limited template variety (3-4 built-in templates); custom templates require subclassing","Responsive behavior is fixed to predefined breakpoints; fine-grained control requires CSS"],"requires":["Python 3.8+","Bokeh for rendering","Optional: CSS knowledge for customization"],"input_types":["Panel components","Template configuration (title, sidebar, etc.)"],"output_types":["Styled HTML layouts","Responsive web pages","Multi-page applications"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_13","uri":"capability://automation.workflow.async.await.support.for.non.blocking.operations.and.concurrent.request.handling","name":"async/await support for non-blocking operations and concurrent request handling","description":"Panel supports async/await patterns in callbacks and widget event handlers, enabling non-blocking operations and concurrent request handling. Developers can define async callback functions that yield control back to the event loop, allowing other requests to be processed while waiting for I/O (database queries, API calls, file operations). The framework uses Tornado's async event loop to manage concurrent connections and execute async callbacks. This is particularly useful for streaming LLM responses and long-running computations.","intents":["Handle long-running operations without blocking other users","Stream LLM responses in real-time without blocking the UI","Make concurrent API calls and database queries efficiently"],"best_for":["Teams building production dashboards with many concurrent users","Developers integrating with external APIs and databases","Data scientists building LLM-powered applications with streaming"],"limitations":["Async code is more complex than synchronous code; requires understanding of event loops","Debugging async code is difficult; errors in async callbacks may not propagate clearly","Not all libraries support async; blocking libraries will still block the event loop"],"requires":["Python 3.8+","Understanding of async/await patterns","Async-compatible libraries (aiohttp, asyncpg, etc.)"],"input_types":["Async callback functions","Async generators for streaming"],"output_types":["Streamed responses","Concurrent request handling","Non-blocking UI updates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_14","uri":"capability://automation.workflow.linking.and.cross.filtering.between.components.through.parameter.synchronization","name":"linking and cross-filtering between components through parameter synchronization","description":"Panel enables linking between components through parameter synchronization, where changes to one component's parameters automatically update linked components. This is implemented through param.Parameterized watching and Bokeh's property system, allowing developers to create cross-filtered dashboards without explicit callbacks. For example, selecting a row in a table can filter a plot, or changing a slider can update multiple visualizations. Linking is declarative and works through shared parameter references.","intents":["Create cross-filtered dashboards where selecting in one component filters others","Build exploratory interfaces with linked visualizations","Enable parameter-driven filtering without explicit callbacks"],"best_for":["Data scientists building exploratory dashboards","Teams creating multi-view data analysis interfaces","Developers avoiding manual callback registration for linked components"],"limitations":["Linking is based on parameter synchronization; complex filtering logic must be in callbacks","Circular dependencies can occur if not carefully designed (A filters B, B filters A)","Performance degrades with many linked components (100+) due to parameter watching overhead"],"requires":["Python 3.8+","param library for parameter watching","Understanding of reactive programming"],"input_types":["param.Parameter objects","Component selections and interactions"],"output_types":["Updated component state","Filtered visualizations","Cross-filtered data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_2","uri":"capability://automation.workflow.layout.system.with.responsive.grid.column.row.and.template.composition","name":"layout system with responsive grid, column, row, and template composition","description":"Panel provides a hierarchical layout system built on Bokeh's GridBox model, enabling developers to compose dashboards using Row, Column, and Grid containers that automatically handle responsive sizing and alignment. Layouts are defined in Python as nested objects (e.g., pn.Column(pn.Row(widget1, widget2), plot)) and compile to Bokeh layout models that render responsively in the browser. The framework also includes pre-built templates (Bootstrap, Material Design, etc.) that provide consistent styling and navigation patterns without CSS knowledge.","intents":["Arrange widgets and visualizations in responsive grid layouts without writing HTML/CSS","Create multi-page dashboards with navigation templates","Build mobile-friendly layouts that adapt to different screen sizes"],"best_for":["Python developers unfamiliar with web layout and CSS","Teams building internal tools that need consistent styling","Data scientists prototyping dashboards quickly without design overhead"],"limitations":["Layout customization beyond provided templates requires CSS knowledge or custom Bokeh models","Responsive behavior is limited to predefined breakpoints; fine-grained media query control is not exposed","Nested layouts add DOM depth which can impact rendering performance in very large dashboards (100+ components)"],"requires":["Python 3.8+","Bokeh for layout rendering","Optional: CSS knowledge for custom styling"],"input_types":["Panel Viewable objects (widgets, panes, layouts)","Python objects (converted to panes automatically)","Markdown strings for text content"],"output_types":["Bokeh GridBox models","HTML/CSS rendered layouts","Responsive browser layouts"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_3","uri":"capability://tool.use.integration.interactive.widget.library.with.input.controls.tables.and.indicators","name":"interactive widget library with input controls, tables, and indicators","description":"Panel provides 30+ interactive widgets (sliders, text inputs, dropdowns, date pickers, tables, progress indicators) that inherit from param.Parameterized and automatically sync with Python state. Widgets are implemented as Bokeh models on the frontend with Python parameter objects on the backend, enabling two-way binding where user interactions update Python values and parameter changes update the UI. Table widgets support Pandas and Polars DataFrames with built-in sorting, filtering, and pagination without additional configuration.","intents":["Create interactive forms and controls for parameter exploration","Display and interact with tabular data (Pandas/Polars) with sorting and filtering","Build progress indicators and status displays that update in real-time"],"best_for":["Data scientists building parameter exploration tools","Teams creating data entry forms and dashboards","Developers needing rich table interactions without JavaScript"],"limitations":["Table widget performance degrades with >10,000 rows; requires server-side pagination for larger datasets","Custom widget styling is limited to Bokeh's theming system; complex CSS customization requires subclassing","Widget validation is basic (type checking, range constraints); complex validation logic must be implemented in callbacks"],"requires":["Python 3.8+","Bokeh for widget rendering","pandas or polars for table widgets"],"input_types":["User interactions (clicks, text input, selections)","Parameter value changes","DataFrame objects (for table widgets)"],"output_types":["Parameter value updates","Callback function executions","Rendered HTML/JavaScript widgets"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_4","uri":"capability://automation.workflow.notebook.to.web.deployment.with.context.agnostic.code.execution","name":"notebook-to-web deployment with context-agnostic code execution","description":"Panel applications are written in pure Python and execute identically in Jupyter notebooks, web servers (via panel serve), and batch scripts without code changes. The framework achieves this through param.Parameterized abstraction which decouples application logic from deployment context. When deployed as a web app, Panel runs a Bokeh server that maintains Python state and handles bidirectional communication with the browser, while in notebooks the same code renders inline using Jupyter's display system.","intents":["Develop dashboards in Jupyter notebooks and deploy them as web apps without refactoring","Share interactive notebooks that work in any environment (local, cloud, batch)","Avoid context-specific code paths (if notebook: ... else: ...) by using deployment-agnostic patterns"],"best_for":["Data scientists iterating in notebooks then deploying to production","Teams sharing reproducible analyses across different execution contexts","Organizations avoiding Streamlit/Dash lock-in by using deployment-agnostic code"],"limitations":["Notebook rendering uses Jupyter's display system which may not support all Bokeh features (e.g., server-side callbacks)","State persistence across notebook cell re-runs requires explicit caching; Panel doesn't auto-cache like Streamlit","Deployment requires Bokeh server infrastructure; Panel apps cannot run as static HTML without JavaScript"],"requires":["Python 3.8+","Jupyter (for notebook execution) or Bokeh server (for web deployment)","param library for parameter declaration"],"input_types":["Python code in notebook cells or .py files","param.Parameterized class definitions"],"output_types":["Jupyter notebook output (inline rendering)","Bokeh server application (web deployment)","Batch script output"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_5","uri":"capability://text.generation.language.chat.component.system.with.streaming.message.rendering.and.llm.integration","name":"chat component system with streaming message rendering and llm integration","description":"Panel provides ChatInterface and ChatMessage components that enable building conversational UIs with streaming support for LLM responses. The chat system handles message history management, automatic scrolling, and token-aware rendering. Developers can integrate OpenAI, Anthropic, or custom LLM APIs by providing a callback function that receives the message history and returns a response (string or async generator for streaming). The framework handles rendering, state management, and message persistence without requiring manual DOM manipulation.","intents":["Build chatbot interfaces that stream LLM responses in real-time","Create conversational data exploration tools with message history","Integrate multiple LLM providers (OpenAI, Anthropic, local models) without provider-specific code"],"best_for":["Teams building LLM-powered dashboards and applications","Data scientists creating conversational analytics interfaces","Developers prototyping chatbot UIs without frontend expertise"],"limitations":["Message history is stored in browser memory; no built-in persistence to database (requires custom implementation)","Streaming requires async/await support; synchronous LLM calls block the UI","Token counting and context window management are not built-in; developers must implement truncation logic"],"requires":["Python 3.8+","LLM API key (OpenAI, Anthropic, etc.) or local model","async/await support for streaming responses"],"input_types":["User text messages","LLM API responses (string or async generator)","Message history (list of dicts with role/content)"],"output_types":["Rendered chat messages in browser","Streamed text output","Message history state"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_6","uri":"capability://text.generation.language.markdown.and.html.rendering.with.embedded.python.expressions","name":"markdown and html rendering with embedded python expressions","description":"Panel's Markdown and HTML panes enable rendering formatted text with embedded Python expressions that update reactively. Developers can write Markdown/HTML strings with f-string-like syntax (e.g., f'Current value: {param_obj.value}') and Panel automatically updates the rendered output when parameters change. This is implemented through string parsing and parameter watching, allowing dynamic content generation without explicit callbacks. Supports both static HTML and reactive Markdown with LaTeX math rendering.","intents":["Display dynamic text content that updates when parameters change","Render formatted documentation and reports with embedded metrics","Create rich text dashboards with LaTeX equations and formatted tables"],"best_for":["Data scientists creating dynamic reports with live metrics","Teams building documentation dashboards","Developers needing formatted text output without HTML expertise"],"limitations":["Expression parsing is limited to simple f-string syntax; complex Python logic must be in callbacks","HTML rendering is not sandboxed; arbitrary HTML/JavaScript can be injected (security risk with untrusted input)","LaTeX rendering requires MathJax library; large equations may impact performance"],"requires":["Python 3.8+","Markdown library (for Markdown pane)","Optional: MathJax for LaTeX rendering"],"input_types":["Markdown strings","HTML strings","f-string expressions with parameter references"],"output_types":["Rendered HTML","Formatted text in browser","LaTeX-rendered equations"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_7","uri":"capability://automation.workflow.server.deployment.with.bokeh.server.backend.and.websocket.communication","name":"server deployment with bokeh server backend and websocket communication","description":"Panel applications are deployed using a Bokeh server backend that maintains Python state and handles bidirectional WebSocket communication with the browser. The panel serve command starts a Bokeh server that loads the Panel application, creates a new Python session per client connection, and synchronizes parameter changes between the browser and Python through Bokeh's protocol. This enables stateful applications where each user has isolated state, unlike stateless frameworks like Streamlit.","intents":["Deploy Panel applications as production web services with isolated user sessions","Enable real-time collaboration with multiple concurrent users","Maintain application state across user interactions without rebuilding"],"best_for":["Teams deploying production dashboards with multiple concurrent users","Organizations needing stateful applications with isolated user sessions","Developers building real-time collaborative tools"],"limitations":["Bokeh server requires Python runtime on the server; cannot be deployed as static HTML","Scaling to many concurrent users requires load balancing and session management (not built-in)","WebSocket communication adds latency compared to static HTML; suitable for <1000 concurrent users"],"requires":["Python 3.8+","Bokeh server (included with Panel)","Optional: Gunicorn, Nginx for production deployment"],"input_types":["Panel application code (.py file)","Configuration parameters (port, address, etc.)"],"output_types":["Running web server","WebSocket connections to clients","Rendered web application in browser"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_8","uri":"capability://tool.use.integration.custom.component.creation.through.viewable.subclassing.and.bokeh.model.binding","name":"custom component creation through viewable subclassing and bokeh model binding","description":"Panel enables developers to create custom components by subclassing the Viewable base class and implementing a _get_model() method that returns a Bokeh model. This allows wrapping custom Bokeh models, JavaScript libraries, or complex UI logic as reusable Panel components. The framework handles parameter synchronization, event propagation, and lifecycle management automatically. Developers can also use Panel's IPyWidget pane to embed Jupyter widgets without creating custom components.","intents":["Wrap custom Bokeh models as reusable Panel components","Integrate third-party JavaScript libraries into Panel dashboards","Create domain-specific components (e.g., custom chart types, specialized controls)"],"best_for":["Teams with existing Bokeh models or JavaScript libraries","Developers building specialized components for internal use","Organizations extending Panel with domain-specific functionality"],"limitations":["Requires understanding of Bokeh model architecture and JavaScript; steep learning curve","Custom components must handle parameter synchronization manually if not using Bokeh's property system","Testing custom components requires both Python and JavaScript test infrastructure"],"requires":["Python 3.8+","Bokeh knowledge for model creation","Optional: JavaScript/TypeScript for custom frontend code"],"input_types":["Bokeh model classes","Python parameter definitions","JavaScript/TypeScript code"],"output_types":["Custom Viewable subclasses","Bokeh models","Rendered components in browser"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-panel__cap_9","uri":"capability://memory.knowledge.state.management.and.caching.with.parameter.watching.and.callback.registration","name":"state management and caching with parameter watching and callback registration","description":"Panel implements state management through param.Parameterized parameter watching and explicit callback registration. Developers can use @pn.depends decorator to create reactive functions that re-execute when dependencies change, or use .watch() to register callbacks on parameter changes. The framework also provides caching mechanisms through param's @param.depends decorator with cache_on argument, enabling memoization of expensive computations. State is maintained in Python memory and synchronized with the browser through Bokeh models.","intents":["Manage application state through parameter watching without explicit event handlers","Cache expensive computations and re-execute only when dependencies change","Create reactive functions that update when parameters change"],"best_for":["Data scientists building parameter-driven analyses with expensive computations","Teams managing complex application state with multiple interdependencies","Developers avoiding manual event handler registration"],"limitations":["Caching is in-memory only; no built-in persistence across server restarts","Parameter watching can create circular dependencies if not carefully designed","Debugging parameter dependencies is difficult with complex graphs; no built-in dependency visualization"],"requires":["Python 3.8+","param library for parameter watching","Understanding of reactive programming patterns"],"input_types":["param.Parameter objects","Python functions decorated with @pn.depends","Callback functions"],"output_types":["Cached computation results","Callback executions","Parameter value updates"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","param library (included as dependency)","Bokeh backend for browser synchronization","Target visualization library (matplotlib, plotly, bokeh, altair, folium, pydeck, pyvista, etc.)","Bokeh for browser rendering","Jupyter notebook or JupyterLab","ipykernel for comm protocol support","pandas or polars library","Bokeh for rendering","Optional: CSS knowledge for customization"],"failure_modes":["Dependency tracking overhead increases with complex parameter graphs — large applications may experience latency in parameter propagation","Requires all interactive state to be declared as param.Parameter objects; imperative state management is not reactive","Bidirectional binding can create circular update loops if not carefully designed","Type detection is based on object class inspection; custom visualization classes may not auto-detect","Rendering performance depends on underlying library — Matplotlib plots are rasterized which limits interactivity","Some libraries (e.g., Plotly) require additional dependencies; Panel doesn't bundle all visualization libraries","Notebook rendering uses Jupyter's comm protocol which may not support all Bokeh features (e.g., server-side callbacks)","Large dashboards in notebooks can slow down notebook performance and increase file size","Notebook state is lost on kernel restart; no built-in persistence","Performance degrades with >10,000 rows; requires server-side pagination for larger datasets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:24.098Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-panel","compare_url":"https://unfragile.ai/compare?artifact=pypi-panel"}},"signature":"j0mQX2htAqQ6ShxpTYhObRqQhsZIkgBBSUizxynPxGY102ryqBhOCVNPKnBfNnh1ZKAi4zBY9Iip3X8MO+3jCg==","signedAt":"2026-06-20T21:24:58.167Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-panel","artifact":"https://unfragile.ai/pypi-panel","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-panel","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}