Csv Analyzer

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name: csv-analyzer description: Analyze CSV/Excel files with natural language. Get statistics, filter rows, find anomalies, generate summaries, and export results. No pandas needed — uses Python stdlib for lightweight operation. author: zacjiang version: 1.0.0 tags: csv, excel, data, analysis, statistics, filter, report, lightweight

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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)

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"