Create Viz

# /create-viz - Create Visualizations

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).

Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design.

## Usage

```
/create-viz <data source> [chart type] [additional instructions]
```

## Workflow

### 1. Understand the Request

Determine:

- **Data source**: Query results, pasted data, CSV/Excel file, or data to be queried
- **Chart type**: Explicitly requested or needs to be recommended
- **Purpose**: Exploration, presentation, report, dashboard component
- **Audience**: Technical team, executives, external stakeholders

### 2. Get the Data

**If data warehouse is connected and data needs querying:**
1. Write and execute the query
2. Load results into a pandas DataFrame

**If data is pasted or uploaded:**
1. Parse the data into a pandas DataFrame
2. Clean and prepare as needed (type conversions, null handling)

**If data is from a previous analysis in the conversation:**
1. Reference the existing data

### 3. Select Chart Type

If the user didn't specify a chart type, recommend one based on the data and question:

| Data Relationship | Recommended Chart |
|---|---|
| Trend over time | Line chart |
| Comparison across categories | Bar chart (horizontal if many categories) |
| Part-to-whole composition | Stacked bar or area chart (avoid pie charts unless <6 categories) |
| Distribution of values | Histogram or box plot |
| Correlation between two variables | Scatter plot |
| Two-variable comparison over time | Dual-axis line or grouped bar |
| Geographic data | Choropleth map |
| Ranking | Horizontal bar chart |
| Flow or process | Sankey diagram |
| Matrix of relationships | Heatmap |

Explain the recommendation briefly if the user didn't specify.

### 4. Generate the Visualization

Write Python code using one of these libraries based on the need:

- **matplotlib + seaborn**: Best for static, publication-quality charts. Default choice.
- **plotly**: Best for interactive charts or when the user requests interactivity.

**Code requirements:**

```python
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Set professional style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")

# Create figure with appropriate size
fig, ax = plt.subplots(figsize=(10, 6))

# [chart-specific code]

# Always include:
ax.set_title('Clear, Descriptive Title', fontsize=14, fontweight='bold')
ax.set_xlabel('X-Axis Label', fontsize=11)
ax.set_ylabel('Y-Axis Label', fontsize=11)

# Format numbers appropriately
# - Percentages: '45.2%' not '0.452'
# - Currency: '$1.2M' not '1200000'
# - Large numbers: '2.3K' or '1.5M' not '2300' or '1500000'

# Remove chart junk
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('chart_name.png', dpi=150, bbox_inches='tight')
plt.show()
```

### 5. Apply Design Best Practices

**Color:**
- Use a consistent, colorblind-friendly palette
- Use color meaningfully (not decoratively)
- Highlight the key data point or trend with a contrasting color
- Grey out less important reference data

**Typography:**
- Descriptive title that states the insight, not just the metric (e.g., "Revenue grew 23% YoY" not "Revenue by Month")
- Readable axis labels (not rotated 90 degrees if avoidable)
- Data labels on key points when they add clarity

**Layout:**
- Appropriate whitespace and margins
- Legend placement that doesn't obscure data
- Sorted categories by value (not alphabetically) unless ther