Plotly

# Plotly Chart Types

Comprehensive guide to chart types organized by category.

## Basic Charts

### Scatter Plots

```python
import plotly.express as px
fig = px.scatter(df, x='x', y='y', color='category', size='size')

# With trendlines
fig = px.scatter(df, x='x', y='y', trendline='ols')
```

### Line Charts

```python
fig = px.line(df, x='date', y='value', color='group')

# Multiple lines from wide-form data
fig = px.line(df, x='date', y=['metric1', 'metric2', 'metric3'])
```

### Bar Charts

```python
# Vertical bars
fig = px.bar(df, x='category', y='value', color='group')

# Horizontal bars
fig = px.bar(df, x='value', y='category', orientation='h')

# Stacked bars
fig = px.bar(df, x='category', y='value', color='group', barmode='stack')

# Grouped bars
fig = px.bar(df, x='category', y='value', color='group', barmode='group')
```

### Pie Charts

```python
fig = px.pie(df, names='category', values='count')

# Donut chart
fig = px.pie(df, names='category', values='count', hole=0.4)
```

### Area Charts

```python
fig = px.area(df, x='date', y='value', color='category')
```

## Statistical Charts

### Histograms

```python
# Basic histogram
fig = px.histogram(df, x='values', nbins=30)

# With marginal plot
fig = px.histogram(df, x='values', marginal='box')  # or 'violin', 'rug'

# 2D histogram
fig = px.density_heatmap(df, x='x', y='y', nbinsx=20, nbinsy=20)
```

### Box Plots

```python
fig = px.box(df, x='category', y='value', color='group')

# Notched box plot
fig = px.box(df, x='category', y='value', notched=True)

# Show all points
fig = px.box(df, x='category', y='value', points='all')
```

### Violin Plots

```python
fig = px.violin(df, x='category', y='value', color='group', box=True, points='all')
```

### Strip/Dot Plots

```python
fig = px.strip(df, x='category', y='value', color='group')
```

### Distribution Plots

```python
# Empirical cumulative distribution
fig = px.ecdf(df, x='value', color='group')

# Marginal distribution
fig = px.scatter(df, x='x', y='y', marginal_x='histogram', marginal_y='box')
```

### Error Bars

```python
fig = px.scatter(df, x='x', y='y', error_y='error', error_x='x_error')

# Using graph_objects for custom error bars
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
    x=[1, 2, 3],
    y=[5, 10, 15],
    error_y=dict(
        type='data',
        array=[1, 2, 3],
        visible=True
    )
))
```

## Scientific Charts

### Heatmaps

```python
# From matrix data
fig = px.imshow(z_matrix, color_continuous_scale='Viridis')

# With graph_objects
fig = go.Figure(data=go.Heatmap(
    z=z_matrix,
    x=x_labels,
    y=y_labels,
    colorscale='RdBu'
))
```

### Contour Plots

```python
# 2D contour
fig = px.density_contour(df, x='x', y='y')

# Filled contour
fig = go.Figure(data=go.Contour(
    z=z_matrix,
    contours=dict(
        coloring='heatmap',
        showlabels=True
    )
))
```

### Ternary Plots

```python
fig = px.scatter_ternary(df, a='component_a', b='component_b', c='component_c')
```

### Log Scales

```python
fig = px.scatter(df, x='x', y='y', log_x=True, log_y=True)
```

### Image Display

```python
import plotly.express as px
fig = px.imshow(img_array)  # img_array from PIL, numpy, etc.
```

## Financial Charts

### Candlestick Charts

```python
import plotly.graph_objects as go
fig = go.Figure(data=[go.Candlestick(
    x=df['date'],
    open=df['open'],
    high=df['high'],
    low=df['low'],
    close=df['close']
)])
```

### OHLC Charts

```python
fig = go.Figure(data=[go.Ohlc(
    x=df['date'],
    open=df['open'],
    high=df['high'],
    low=df['low'],
    close=df['close']
)])
```

### Waterfall Charts

```python
fig = go.Figure(go.Waterfall(
    x=categories,
    y=values,
    measure=['relative', 'relative', 'total', 'relative', 'total']
))
```

### Funnel Charts

```python
fig = px.funnel(df, x='count', y='stage')

# Or with graph_objects
fig = go.Figure(go.Funnel(
    y=['Stage 1', 'Stage 2', 'Stage 3'],
    x=[100, 60, 40]
))
``