csv-data-visualizer

What this skill does

# CSV Data Visualizer

## Overview

This skill enables comprehensive data visualization and analysis for CSV files. It provides three main capabilities: (1) creating individual interactive visualizations using Plotly, (2) automatic data profiling with statistical summaries, and (3) generating multi-plot dashboards. The skill is optimized for exploratory data analysis, statistical reporting, and creating presentation-ready visualizations.

## When to Use This Skill

Invoke this skill when users request:
- "Visualize this CSV data"
- "Create a histogram/scatter plot/box plot from this data"
- "Show me the distribution of [column]"
- "Generate a dashboard for this dataset"
- "Profile this CSV file" or "Analyze this data"
- "Create a correlation heatmap"
- "Show trends over time"
- "Compare [variable] across [categories]"

## Core Capabilities

### 1. Individual Visualizations

Create specific chart types for detailed analysis using the `visualize_csv.py` script.

**Available Chart Types:**

**Statistical Plots:**
```bash
# Histogram - distribution of numeric data
python3 scripts/visualize_csv.py data.csv --histogram column_name --bins 30

# Box plot - show quartiles and outliers
python3 scripts/visualize_csv.py data.csv --boxplot column_name

# Box plot grouped by category
python3 scripts/visualize_csv.py data.csv --boxplot salary --group-by department

# Violin plot - distribution with probability density
python3 scripts/visualize_csv.py data.csv --violin column_name --group-by category
```

**Relationship Analysis:**
```bash
# Scatter plot with automatic trend line
python3 scripts/visualize_csv.py data.csv --scatter height weight

# Scatter plot with color and size encoding
python3 scripts/visualize_csv.py data.csv --scatter x y --color category --size value

# Correlation heatmap for all numeric columns
python3 scripts/visualize_csv.py data.csv --correlation
```

**Time Series:**
```bash
# Line chart for single variable
python3 scripts/visualize_csv.py data.csv --line date sales

# Multiple variables on same chart
python3 scripts/visualize_csv.py data.csv --line date "sales,revenue,profit"
```

**Categorical Data:**
```bash
# Bar chart (counts categories automatically)
python3 scripts/visualize_csv.py data.csv --bar category

# Pie chart for composition
python3 scripts/visualize_csv.py data.csv --pie region
```

**Output Formats:**
Specify output file with desired format extension:
```bash
# Interactive HTML (default)
python3 scripts/visualize_csv.py data.csv --histogram age -o output.html

# Static image formats
python3 scripts/visualize_csv.py data.csv --scatter x y -o plot.png
python3 scripts/visualize_csv.py data.csv --correlation -o heatmap.pdf
python3 scripts/visualize_csv.py data.csv --bar category -o chart.svg
```

### 2. Automatic Data Profiling

Generate comprehensive data quality and statistical reports using the `data_profile.py` script.

**Text Report (default):**
```bash
python3 scripts/data_profile.py data.csv
```

**HTML Report:**
```bash
python3 scripts/data_profile.py data.csv -f html -o report.html
```

**JSON Report:**
```bash
python3 scripts/data_profile.py data.csv -f json -o profile.json
```

**What the Profiler Provides:**
- File information (size, dimensions)
- Dataset overview (shape, memory usage, duplicates)
- Column-by-column analysis (types, missing data, unique values)
- Missing data patterns and completeness
- Statistical summary for numeric columns (mean, std, quartiles, skewness, kurtosis)
- Categorical column analysis (frequency counts, most/least common values)
- Data quality checks (high missing data, duplicate rows, constant columns, high cardinality)

**When to Use Profiling:**
Always recommend running data profiling BEFORE creating visualizations when:
- User is unfamiliar with the dataset
- Data quality is unknown
- Need to identify appropriate visualization types
- Exploring a new dataset for the first time

### 3. Multi-Plot Dashboards

Create comprehensive dashboards with multiple visualizations using the `create_dashboard.py` script.

**Automatic Dashboard:**
Analyzes data types and automatically creates appropriate visualizations:
```bash
python3 scripts/create_dashboard.py data.csv
```

Custom output location:
```bash
python3 scripts/create_dashboard.py data.csv -o my_dashboard.html
```

Control number of plots:
```bash
python3 scripts/create_dashboard.py data.csv --max-plots 9
```

**Custom Dashboard from Config:**
Create a JSON configuration file specifying exact plots:
```bash
python3 scripts/create_dashboard.py data.csv --config config.json
```

**Dashboard Config Format:**
```json
{
  "title": "Sales Analysis Dashboard",
  "plots": [
    {"type": "histogram", "column": "revenue"},
    {"type": "box", "column": "revenue", "group_by": "region"},
    {"type": "scatter", "column": "advertising", "group_by": "revenue"},
    {"type": "bar", "column": "product_category"},
    {"type": "correlation"}
  ]
}
```

**Dashboard Plot Types:**
- `histogram`: Distribution of numeric column
- `box`: Box plot, optionally grouped by category
- `scatter`: Relationship between two numeric columns
- `bar`: Count of categorical values
- `correlation`: Heatmap of numeric correlations

## Workflow Decision Tree

Use this decision tree to determine the appropriate approach:

```
User provides CSV file
│
├─ "Profile this data" / "Analyze this data" / Unfamiliar dataset
│  └─> Run data_profile.py first
│     Then offer visualization options based on findings
│
├─ "Create dashboard" / "Overview of the data" / Multiple visualizations needed
│  ├─ User knows exact plots wanted
│  │  └─> Create JSON config → run create_dashboard.py with config
│  └─ User wants automatic dashboard
│     └─> Run create_dashboard.py (auto mode)
│
└─ Specific visualization requested ("histogram", "scatter plot", etc.)
   └─> Use visualize_csv.py with appropriate flag
```

## Best Practices

### Starting Analysis
1. **Always profile first** for unfamiliar datasets: `python3 scripts/data_profile.py data.csv`
2. Review the profiling output to understand:
   - Column data types and ranges
   - Missing data patterns
   - Data quality issues
   - Statistical distributions

### Choosing Visualizations
Consult `references/visualization_guide.md` for detailed guidance. Quick reference:
- **Distribution**: Histogram, box plot, violin plot
- **Relationship**: Scatter plot, correlation heatmap
- **Time series**: Line chart
- **Categories**: Bar chart (preferred) or pie chart (use sparingly)
- **Comparison**: Box plot grouped by category

### Creating Dashboards
- **Automatic dashboard**: Good for initial exploration
- **Custom dashboard**: Better for presentations or specific analysis goals
- **Limit plots**: Keep to 6-9 plots maximum for readability
- **Logical grouping**: Group related visualizations together

### Output Considerations
- **HTML**: Best for interactive exploration (zoom, pan, hover tooltips)
- **PNG/PDF**: Best for reports and presentations
- **SVG**: Best for publications requiring vector graphics

## Dependencies

The scripts require these Python packages:
```bash
pip install pandas plotly numpy
```

For static image export (PNG, PDF, SVG), also install:
```bash
pip install kaleido
```

## Example Workflows

### Exploratory Data Analysis
```bash
# 1. Profile the data
python3 scripts/data_profile.py sales_data.csv -f html -o profile.html

# 2. Create automatic dashboard
python3 scripts/create_dashboard.py sales_data.csv -o dashboard.html

# 3. Dive deeper with specific plots
python3 scripts/visualize_csv.py sales_data.csv --scatter price sales --color region
python3 scripts/visualize_csv.py sales_data.csv --boxplot revenue --group-by product
```

### Report Generation
```bash
# Create specific visualizations for report
python3 scripts/visualize_csv.py data.csv --histogram age -o fig1_distribution.png
python3 scripts/visualize_csv.py data.csv --scatter income age -o fig2_correlation.png
python3 scripts/visualize_csv.py data.csv --bar category