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#

text

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#

bash

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.