plotly-interactive-visualization

# Plotly — Interactive Scientific Visualization

## Overview

Plotly is a Python graphing library for interactive, web-embeddable visualizations with 40+ chart types. It provides two APIs: Plotly Express (high-level, pandas-native) for quick plots and Graph Objects (low-level) for full customization. Output to interactive HTML, static PNG/PDF/SVG, or Dash web apps.

## When to Use

- Creating interactive charts with hover tooltips, zoom, and pan
- Building multi-panel exploratory dashboards for data analysis
- Visualizing 3D data (surfaces, scatter3d, mesh, volume)
- Making geographic/map visualizations (choropleth, scatter_geo)
- Presenting data in web-embeddable HTML format
- Statistical distribution comparison (violin, box, histogram with marginals)
- Time series with range sliders and animation frames
- For **static publication-quality figures** (journal submissions), use `matplotlib` instead
- For **statistical grammar-of-graphics** style, use `seaborn` instead

## Prerequisites

- **Python packages**: `plotly`, `pandas`, `numpy`
- **For static export**: `kaleido` (PNG/PDF/SVG rendering)
- **For web apps**: `dash` (optional)

```bash
pip install plotly kaleido
```

## Quick Start

```python
import plotly.express as px
import pandas as pd
import numpy as np

# Sample data
np.random.seed(42)
df = pd.DataFrame({
    "x": np.random.randn(200),
    "y": np.random.randn(200),
    "group": np.random.choice(["A", "B", "C"], 200),
    "size": np.random.uniform(5, 20, 200),
})

fig = px.scatter(df, x="x", y="y", color="group", size="size",
                 title="Interactive Scatter Plot", hover_data=["group"])
fig.write_html("scatter.html")
fig.write_image("scatter.png", width=800, height=500, scale=2)
print("Saved scatter.html and scatter.png")
```

## Core API

### 1. Plotly Express (High-Level API)

Quick, one-line charts from pandas DataFrames. Returns `go.Figure` objects that can be further customized.

```python
import plotly.express as px
import pandas as pd
import numpy as np

np.random.seed(42)
df = pd.DataFrame({
    "temperature": np.linspace(20, 80, 50),
    "yield": 50 + 0.8 * np.linspace(20, 80, 50) + np.random.randn(50) * 5,
    "catalyst": np.random.choice(["Pd", "Pt", "Rh"], 50),
})

# Scatter with trendline
fig = px.scatter(df, x="temperature", y="yield", color="catalyst",
                 trendline="ols", title="Temperature vs Yield")
fig.write_image("scatter_trend.png", width=700, height=450)
print("Saved scatter_trend.png")

# Bar chart
summary = df.groupby("catalyst")["yield"].mean().reset_index()
fig = px.bar(summary, x="catalyst", y="yield", color="catalyst",
             title="Mean Yield by Catalyst")
fig.write_image("bar_catalyst.png", width=600, height=400)
print("Saved bar_catalyst.png")
```

```python
# Heatmap from correlation matrix
import plotly.express as px
import pandas as pd
import numpy as np

np.random.seed(42)
data = pd.DataFrame(np.random.randn(100, 5), columns=["Gene_A", "Gene_B", "Gene_C", "Gene_D", "Gene_E"])
corr = data.corr()

fig = px.imshow(corr, text_auto=".2f", color_continuous_scale="RdBu_r",
                zmin=-1, zmax=1, title="Gene Expression Correlation")
fig.write_image("heatmap.png", width=600, height=500)
print("Saved heatmap.png")
```

### 2. Graph Objects (Low-Level API)

Full control over individual traces, layouts, and annotations.

```python
import plotly.graph_objects as go
import numpy as np

# 3D surface plot
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

fig = go.Figure(data=[go.Surface(z=Z, x=X[0], y=y, colorscale="Viridis")])
fig.update_layout(title="3D Surface Plot",
                  scene=dict(xaxis_title="X", yaxis_title="Y", zaxis_title="Z"))
fig.write_image("surface_3d.png", width=700, height=500)
print("Saved surface_3d.png")
```

```python
# Multi-trace figure with custom styling
import plotly.graph_objects as go
import numpy as np

np.random.seed(42)
x = np.linspace(0, 10, 100)
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=np.sin(x), mode="lines", name="sin(x)",
                         line=dict(color="blue", width=2)))
fig.add_trace(go.Scatter(x=x, y=np.cos(x), mode="lines", name="cos(x)",
                         line=dict(color="red", width=2, dash="dash")))
fig.add_hline(y=0, line_dash="dot", line_color="gray", opacity=0.5)
fig.add_annotation(x=np.pi/2, y=1, text="sin peak", showarrow=True, arrowhead=2)

fig.update_layout(template="plotly_white", title="Trigonometric Functions",
                  xaxis_title="x", yaxis_title="f(x)")
fig.write_image("multi_trace.png", width=700, height=400)
print("Saved multi_trace.png")
```

### 3. Subplots and Multi-Panel Layouts

Create figure grids with shared or independent axes.

```python
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np

np.random.seed(42)
data = np.random.randn(500)

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=("Histogram", "Box Plot", "Scatter", "Violin"),
    specs=[[{"type": "histogram"}, {"type": "box"}],
           [{"type": "scatter"}, {"type": "violin"}]],
)

fig.add_trace(go.Histogram(x=data, nbinsx=30, name="Hist"), row=1, col=1)
fig.add_trace(go.Box(y=data, name="Box"), row=1, col=2)
fig.add_trace(go.Scatter(x=data[:100], y=data[100:200], mode="markers", name="Scatter"), row=2, col=1)
fig.add_trace(go.Violin(y=data, name="Violin", box_visible=True), row=2, col=2)

fig.update_layout(height=700, width=800, title_text="Multi-Panel Dashboard", showlegend=False)
fig.write_image("subplots.png", width=800, height=700)
print("Saved subplots.png")
```

### 4. Statistical Charts

Distribution comparison, error bars, and statistical annotations.

```python
import plotly.express as px
import pandas as pd
import numpy as np

np.random.seed(42)
df = pd.DataFrame({
    "value": np.concatenate([np.random.normal(0, 1, 100), np.random.normal(2, 1.5, 100)]),
    "group": ["Control"] * 100 + ["Treatment"] * 100,
})

# Histogram wi