专业分流成绩可视化分析（通用版）

To visualize professional distribution scores from Excel data, you can automate analysis to generate density plots, alluvial diagrams, and scatter plots directly from your spreadsheets.

Generating density plots and alluvial diagrams for score analysis requires pandas, openpyxl, matplotlib, seaborn, plotly, scipy, and statsmodels for processing and visualization.

Yes, you can analyze student performance and professional distribution trends by processing Excel sheets like '成绩排名表.xlsx' and '分流结果表.xlsx' to automate score visualization.

This score visualization approach supports both interactive charts and static outputs by utilizing plotly for interactivity alongside matplotlib, seaborn, and optional kaleido for static images.

The best way to compare professional distribution results across cohorts is by generating alluvial diagrams and density plots to visually track score trends and allocation flows.

Processing large Excel files for automated score analysis depends on memory constraints of pandas and openpyxl, and generating complex plotly visualizations may require significant computational resources.