Principle:Datajuicer Data juicer Column Wise Distribution Analysis
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Statistics, Visualization |
| Last Updated | 2026-02-14 17:00 GMT |
Overview
A per-column visualization technique that generates distribution plots (histograms and box plots) for each quality metric to reveal data characteristics.
Description
Column-Wise Distribution Analysis generates individual distribution visualizations for each statistics column in a dataset. For numeric columns, it produces histograms with optional quantile overlay lines and box plots. For categorical or text-based columns, it generates word clouds. These visualizations enable users to understand the shape of each quality metric's distribution, identify multimodal patterns, spot outliers, and select appropriate filter thresholds visually.
Usage
Use this principle after overall analysis to drill down into individual metric distributions. It provides the visual context that summary statistics alone cannot convey (e.g., bimodal distributions, heavy tails).
Theoretical Basis
For each statistics column:
- Determine column type (numeric vs categorical)
- For numeric: compute histogram bins, render histogram + box plot
- For categorical/text: compute term frequencies, render word cloud
- Optionally overlay quantile lines from overall analysis