Bytesagain Data Analytics

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.

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 categories
• correlate — Pearson correlation matrix across all numeric columns
• top — Rank top 15 values in any column with percentage and bar chart
• trend — ASCII line chart showing value trend over rows with direction indicator
• clean — Data quality report: null counts, low cardinality, coverage per column
• pivot — 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 revenue

Requirements

• 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.