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:

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:

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:

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:

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:

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):

python3 scripts/data_profile.py data.csv

HTML Report:

python3 scripts/data_profile.py data.csv -f html -o report.html

JSON Report:

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:

python3 scripts/create_dashboard.py data.csv

Custom output location:

python3 scripts/create_dashboard.py data.csv -o my_dashboard.html

Control number of plots:

python3 scripts/create_dashboard.py data.csv --max-plots 9

Custom Dashboard from Config: Create a JSON configuration file specifying exact plots:

python3 scripts/create_dashboard.py data.csv --config config.json

Dashboard Config Format:

{ "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:

pip install pandas plotly numpy

For static image export (PNG, PDF, SVG), also install:

pip install kaleido

Example Workflows

Exploratory Data Analysis

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

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

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