panel-dashboards

# Panel Dashboards Skill

## Overview

Master interactive dashboard and application development with Panel and Param. This skill covers building web applications, component systems, and responsive dashboards that scale from simple tools to complex enterprise applications.

## Dependencies

- panel >= 1.3.0
- param >= 2.0.0
- bokeh >= 3.0.0
- tornado (web server)

## Core Capabilities

### 1. Component-Based Application Development

Panel provides a comprehensive component library for building rich user interfaces:

- **Layout Components**: Row, Column, Tabs, Accordion, GridBox
- **Input Widgets**: TextInput, Select, DatePicker, RangeSlider, FileInput
- **Output Display**: Markdown, HTML, DataFrame, Image, Video
- **Container Controls**: Card, Alert, ProgressBar

```python
import panel as pn
import param

pn.extension('material')

class Dashboard(param.Parameterized):
    title = param.String(default="My Dashboard")
    refresh_interval = param.Integer(default=5000, bounds=(1000, 60000))

    @param.depends('refresh_interval')
    def view(self):
        return pn.Column(
            pn.pane.Markdown(f"## {self.title}"),
            pn.param.ObjectSelector.from_param(self.param.refresh_interval),
            pn.Row(self._metric_card(), self._chart())
        )

    def _metric_card(self):
        return pn.Card(
            "Active Users",
            "42,531",
            title="Metrics",
            styles={"background": "#E8F4F8"}
        )

    def _chart(self):
        return pn.pane.Markdown("## Chart Placeholder")

dashboard = Dashboard()
app = dashboard.view

if __name__ == '__main__':
    app.servable()
```

### 2. Reactive Pipelines and Watchers

Panel excels at creating reactive, event-driven applications:

```python
import panel as pn
import param
import numpy as np

class DataAnalyzer(param.Parameterized):
    data_source = param.Selector(default='random', objects=['random', 'file'])
    num_points = param.Integer(default=100, bounds=(10, 1000))
    aggr