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
- 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