Analyze categorical data with charts

Analyze categorical data with comparison stats and charts

Quick categorical comparison analysis when data is spread across Excel sheets and visual charts (bar + pie) help identify distribution patterns.

• · Excel file (.xlsx) with data across multiple sheets
• · two categorical dimensions to compare
• · row count per sheet (to assess if large-file optimization needed)

• · total row count across all sheets
• · data cleaned: merged cells filled (ffill), empty values dropped, placeholder rows excluded
• · categorization statistics: count per category, difference, percent distribution
• · multi-dimensional comparison table
• · bar chart (matplotlib, colorized, labeled)
• · pie chart (matplotlib, percentage labels)
• · Excel export of analysis report
• · download link to report

• · pandas (read_excel, ffill, groupby, count)
• · matplotlib (bar chart, pie chart, Chinese font config)

• · Excel file (.xlsx) with multiple sheets
• · Two categorical dimensions identified for comparison
• · File size assessed (total row count determines optimization strategy)

• · Merged cells not handled (data cells treated as empty)
• · Placeholder rows ('代码', '名称') not excluded (inflates counts)
• · Empty values not dropped (distorts statistics)
• · Comparison table is sparse or missing (no aggregation)
• · Charts lack labels or units (hard to interpret)
• · Chinese font not configured in matplotlib (mojibake output)
• · Large files processed without streaming (memory overflow)

• · Five-step workflow: count rows → clean (ffill + exclude) → aggregate → visualize → export
• · Chinese font configuration explicit (plt.rcParams for SimHei/DejaVu)
• · DataFrame operations named: ffill (merged cells), dropna, groupby, count
• · Two visualization types: bar chart (with value labels) + pie chart (with percentages)
• · Merged cell handling documented (ffill strategy)
• · Placeholder exclusion pattern ('代码' as exclude_val example)