dvcrn CSV Analyzer
Analyze CSV files with simple commands. Get instant statistics, filter data, detect anomalies, and export results — all without pandas or heavy dependencies.
Usage
Quick stats
python3 {baseDir}/scripts/csv_analyze.py stats data.csv
Shows row count, column types, min/max/mean for numeric columns, unique counts for text columns.
Filter rows
python3 {baseDir}/scripts/csv_analyze.py filter data.csv --where "amount>1000" --output big_orders.csv
Top/Bottom N
python3 {baseDir}/scripts/csv_analyze.py top data.csv --column revenue --n 10
python3 {baseDir}/scripts/csv_analyze.py bottom data.csv --column revenue --n 5
Detect anomalies (values outside 2σ)
python3 {baseDir}/scripts/csv_analyze.py anomalies data.csv --column price
Group and aggregate
python3 {baseDir}/scripts/csv_analyze.py group data.csv --by category --agg "sum:amount" "count:id"
Features
- 📊 Automatic column type detection (numeric, date, text)
- 🔍 Flexible filtering with comparison operators
- 📈 Statistical summary (mean, median, std, min, max, percentiles)
- 🚨 Anomaly detection (z-score based)
- 📋 Grouping and aggregation
- 💾 Export filtered/processed results
- 🪶 Zero external dependencies — Python stdlib only (csv module)
Dependencies
None! Uses only Python standard library.
Why Not Pandas?
Pandas is great but:
- Takes 100MB+ RAM just to import
- Overkill for quick analysis tasks
- This skill runs on 2GB RAM servers without issues
- For truly large datasets, the agent can recommend installing pandas
Limitations
- Designed for files up to ~100MB (loads into memory)
- For larger files, use streaming mode or install pandas
- Date parsing is basic (ISO format preferred)