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

Always profile first for unfamiliar datasets:
```
python3 scripts/data_profile.py data.csv
```
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 -o fig3_categories.png

# Generate data summary
python3 scripts/data_profile.py data.csv -f html -o data_summary.html

Interactive Dashboard

bash

# Create custom dashboard for presentation
# 1. First, create config.json with desired plots
# 2. Generate dashboard
python3 scripts/create_dashboard.py data.csv --config config.json -o presentation_dashboard.html

Troubleshooting

"Column not found" errors:

Run data profiling to see exact column names
CSV columns are case-sensitive
Check for leading/trailing spaces in column names

Empty or incorrect visualizations:

Verify data types (numeric vs categorical)
Check for missing data in plotted columns
Ensure sufficient non-null values exist

Script execution errors:

Verify dependencies are installed:
```
pip list | grep plotly
```
Check Python version: Python 3.6+ required
For image export issues, install kaleido:
```
pip install kaleido
```

Resources

scripts/

```
visualize_csv.py
```
: Main visualization script with all chart types
```
data_profile.py
```
: Automatic data profiling and quality analysis
```
create_dashboard.py
```
: Multi-plot dashboard generator

references/

```
visualization_guide.md
```
: Comprehensive guide for choosing appropriate chart types, best practices, and common patterns

csv-data-visualizer

NPX Install

Tags

SKILL.md Content

CSV Data Visualizer

Overview

When to Use This Skill

Core Capabilities

1. Individual Visualizations

2. Automatic Data Profiling

3. Multi-Plot Dashboards

Workflow Decision Tree

Best Practices

Starting Analysis

Choosing Visualizations

Creating Dashboards

Output Considerations

Dependencies

Example Workflows

Exploratory Data Analysis

Report Generation

Interactive Dashboard

Troubleshooting

Resources

scripts/

references/