parameterization

# Parameterization Skill

## Overview

Master declarative parameter systems with Param and dynamic UI generation. This skill covers building flexible, type-safe, and auto-validated application logic.

## Dependencies

- param >= 2.0.0
- panel >= 1.3.0 (for UI generation)
- numpy >= 1.15.0
- pandas >= 1.0.0

## Core Capabilities

### 1. Parameter Basics

Param provides a framework for parameterized objects with automatic validation:

```python
import param
import numpy as np

class DataProcessor(param.Parameterized):
    # Basic parameters
    name = param.String(default='Processor', doc='Name of processor')
    count = param.Integer(default=10, bounds=(1, 1000), doc='Number of items')
    scale = param.Number(default=1.0, bounds=(0.1, 10.0), doc='Scale factor')

    # String choices
    method = param.Selector(default='mean', objects=['mean', 'median', 'sum'])

    # Boolean flag
    normalize = param.Boolean(default=False)

    # List or array
    tags = param.List(default=[], item_type=str)
    data_array = param.Array(default=np.array([]))

# Instantiate and use
processor = DataProcessor()
print(f"Name: {processor.name}, Count: {processor.count}")

# Validate parameters automatically
processor.count = 500  # OK
processor.count = 2000  # Raises error: out of bounds
```

### 2. Advanced Parameter Types

```python
class AdvancedConfig(param.Parameterized):
    # Date/time parameters
    date = param.Date(default='2024-01-01', doc='Start date')
    time = param.Time(default='12:00', doc='Start time')
    datetime = param.DateTime(default='2024-01-01 12:00:00')

    # File/path parameters
    input_file = param.Path(default=None, doc='Input file path')
    output_dir = param.Fspath(default='.', doc='Output directory')

    # Range parameter
    value_range = param.Range(default=(0, 10), bounds=(0, 100))

    # Color parameter
    color = param.Color(default='#FF0000')

    # JSON/Dict parameter
    config = param.Dict(default={}, per_instance=True)

    # DataFrame pa