# Data Viz

> Generates professional statistical charts (Bar, Pie, Grouped) using Matplotlib and Seaborn. Use this skill to visualize survey data, trends, and distributions for reports.

- Skill: `tomevault-io/data-viz` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/data-viz`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/data-viz/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/data-viz

---


# Data Viz Skill

This skill provides a standardized way to generate high-quality statistical charts for reports. It handles styling (using Sebrae-compatible colors), layout, and saving to files.

## Capabilities

### 1. Bar Charts (`plot_bar`)
Best for comparing categories or counts. Supports vertical and horizontal orientation.

### 2. Pie Charts (`plot_pie`)
Best for showing composition (shares) of a whole. Limit to Top 5-7 categories for readability.

### 3. Grouped Bar Charts (`plot_grouped_bar`)
Best for comparing distributions across segments.

### 4. Evolution Line Charts (Survey Specific)
Best for comparing means of domains across points in time (e.g., Start vs End). Use `plot_evolution_line`.

### 5. Word Clouds
For qualitative text analysis visualization. Use in conjunction with `survey-qual-analyzer` frequencies.

### 6. Multivariate Analysis (`principal_component_plotting`, `correlation_ellipse_plot`, `multivariate_normal_contours`)
- **PCA**: Scree plots, loadings, and biplots.
- **Correlation**: Ellipse plots for correlation matrices (publication style).
- **Probability**: Bivariate normal distribution contours.

### 7. Performance Visualization (`performance_curve_builder`)
- **Performance Curves**: Cumulative results over time for any strategy.
- **Drawdown**: Visualizing risk periods and recovery.

## Usage

```python
import pandas as pd
# Import from skill scripts directory
from scripts.plotter import plot_bar, plot_pie, plot_grouped_bar
from scripts.evolution_plotter import plot_evolution_line
from scripts.visuals import *  # Additional visualization utilities
from scripts.advanced_plots import *  # Advanced chart types
from scripts.principal_component_plotting import plot_feature_importance, plot_biplot 
from scripts.correlation_ellipse_plot import plot_corr_ellipses
from scripts.multivariate_normal_contours import plot_contours
from scripts.performance_curve_builder import calculate_drawdown, extend_series_to_date

# 1. Simple Bar Chart (Top 10 Cities)
plot_bar(df, x_col="City", title="Respondents by City", filename="output/city_dist.png", orientation='h')

# 2. Evolution of Domains (Survey Pre vs Post)
# Expected columns: 'Cycle', 'Domain', 'Mean'
plot_evolution_line(df_evo, x="Cycle", y="Mean", hue="Domain", title="Evolution of Domains", filename="output/evolution.png")
```

## Aesthetic Standards & Surveys
- **Palette**: Dark blue, cyan, and neutral grays for contrast.
- **Labels**: Always include sample size (n) if available.
- **Premium**: High DPI (300) and clean backgrounds for publication-ready reports.

## Dependencies
Requires `matplotlib`, `seaborn`, and `pandas`.

---
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<!-- tomevault:4.0:skill_md:2026-04-11 -->