This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive

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: Relationship Analysis: Time Series: Categorical Data: Output Formats: Specify output file with desired format extension: 2. Automatic Data Profiling Generate comprehensive data quality and statistical reports using the data profile.py script. Text Report (default): HTML Report: JSON Report: 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: Custom output location: Control number of plots: Custom Dashboard from Config: Create a JSON configuration file specifying exact plots: Dashboard Config Format: 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: 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: For static image export (PNG, PDF, SVG), also install: Example Workflows Exploratory Data Analysis Report Generation Interactive Dashboard 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