- description
- Analyze CSV files with statistical summaries, correlations, and pivot tables. Use when exploring new datasets, checking data quality, finding column correlations, ranking top values, or charting trends.
- author
- BytesAgain
- homepage
- https://bytesagain.com
- source
- https://github.com/bytesagain/ai-skills
bytesagain-data-analytics
Terminal data analysis toolkit for CSV files. Compute statistical summaries, correlation matrices, top value rankings, trend charts, data quality reports, and pivot tables — no Python data science libraries required.
Usage
bytesagain-data-analytics describe <csv_file>
bytesagain-data-analytics correlate <csv_file>
bytesagain-data-analytics top <csv_file> <column>
bytesagain-data-analytics trend <csv_file> <column>
bytesagain-data-analytics clean <csv_file>
bytesagain-data-analytics pivot <csv_file> <row_col> <value_col>Commands
describe— Per-column statistics: count, mean, std, percentiles, top categoriescorrelate— Pearson correlation matrix across all numeric columnstop— Rank top 15 values in any column with percentage and bar charttrend— ASCII line chart showing value trend over rows with direction indicatorclean— Data quality report: null counts, low cardinality, coverage per columnpivot— Group by a category column and aggregate a numeric column
Examples
bytesagain-data-analytics describe sales.csv
bytesagain-data-analytics correlate metrics.csv
bytesagain-data-analytics top customers.csv country
bytesagain-data-analytics trend revenue.csv amount
bytesagain-data-analytics clean user-data.csv
bytesagain-data-analytics pivot orders.csv category revenueRequirements
- bash
- python3
When to Use
Use when exploring a new dataset, checking data quality before analysis, finding correlations between metrics, or generating quick visual summaries from CSV exports without opening a spreadsheet.