plotting-fundamentals

# Plotting Fundamentals Skill

## Overview

Master quick plotting and interactive visualization with hvPlot and HoloViews basics. This skill covers essential techniques for creating publication-quality plots with minimal code.

## Dependencies

- hvplot >= 0.9.0
- holoviews >= 1.18.0
- pandas >= 1.0.0
- numpy >= 1.15.0
- bokeh >= 3.0.0

## Core Capabilities

### 1. hvPlot Quick Plotting

hvPlot provides an intuitive, pandas-like API for rapid visualization:

```python
import hvplot.pandas
import pandas as pd
import numpy as np

# Create sample data
df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=100),
    'sales': np.cumsum(np.random.randn(100)) + 100,
    'region': np.random.choice(['North', 'South', 'East', 'West'], 100)
})

# Simple line plot
df.hvplot.line(x='date', y='sales', title='Sales Over Time')

# Grouped plot
df.hvplot.line(x='date', y='sales', by='region', subplots=True)

# Scatter with size and color
df.hvplot.scatter(x='sales', y='date', c='region', size=50)
```

### 2. Common Plot Types

```python
# Bar plot
df.hvplot.bar(x='region', y='sales', rot=45)

# Histogram
df['sales'].hvplot.hist(bins=30, title='Sales Distribution')

# Box plot
df.hvplot.box(y='sales', by='region')

# Area plot
df.hvplot.area(x='date', y='sales')

# KDE (Kernel Density Estimation)
df['sales'].hvplot.kde()

# Hexbin (for large datasets)
df.hvplot.hexbin(x='sales', y='date', gridsize=20)
```

### 3. Customization Options

```python
# Apply consistent styling
plot = df.hvplot.line(
    x='date',
    y='sales',
    title='Sales Trend',
    xlabel='Date',
    ylabel='Sales ($)',
    color='#2E86DE',
    line_width=2,
    height=400,
    width=700,
    responsive=True,
    legend='top_left'
)

# Color mapping
df.hvplot.scatter(
    x='sales',
    y='date',
    c='sales',
    cmap='viridis',
    s=100
)

# Multiple series
df.hvplot.line(
    x='date',
    y=['sales'],
    title='Performance Metrics'
)
```

### 4. Interactive Features

```python
# Hover informati