csv_analyzer

Analyze tabular data files (CSV, TSV, Excel) using pandas.

When to Use

✅ USE this skill when:

• "Show me what's in data.csv"
• "First 20 rows of sales.xlsx"
• "Statistics for revenue column"
• "Filter rows where age > 30"
• "Average sales by region"
• User wants to explore, summarize, filter, or aggregate tabular data

When NOT to Use

❌ DON'T use this skill when:

• Database queries (SQL) → use database tools
• Writing new CSV/Excel files → use code or spreadsheet tools
• Non-tabular formats (JSON, XML, etc.) → use appropriate parsers

Usage/Commands

python {skill_path}/analyze.py PATH [command] [options]

Commands:

• info (default) — column types, shape, missing values
• head — first N rows (default 10)
• stats — descriptive statistics for numeric columns
• query — filter rows with a pandas query expression
• groupby — group-by aggregation
• columns — list column names and types

Options:

• --rows N — number of rows for head (default 10)
• --query "col > 100" — pandas query expression
• --groupby COL — column to group by
• --agg mean|sum|count|min|max — aggregation function (default: mean)
• --format json — output as JSON
• --columns "col1,col2" — select specific columns

Examples

• "Show me what's in data.csv" → python {skill_path}/analyze.py data.csv info
• "First 20 rows of sales.xlsx" → python {skill_path}/analyze.py sales.xlsx head --rows 20
• "Average sales by region" → python {skill_path}/analyze.py data.csv groupby --groupby region --columns sales --agg mean

Notes

• Install dependencies: pip install pandas openpyxl
• openpyxl required for Excel (.xlsx) support