category-statistics

Generating category distribution charts from Excel data involves extracting a categorical column, computing counts and percentage shares, and producing high-resolution combo charts. This process uses pandas to clean labels and calculate proportions, then matplotlib to output visual reports.

Counting categorical values and calculating proportions in a spreadsheet is best handled by applying data cleaning to remove invalid labels, tallying each class, and dividing by the total. This yields validated category statistics showing the relative percentage share of every group.

Yes, you can use pandas and matplotlib to create survey analysis reports with pie and bar charts. The workflow computes category counts and proportions from survey results, then generates multi-figure report images combining horizontal bar charts with labeling and dual-axis share curves.

Category distribution extraction works with messy tags and invalid labels in CSV files by applying automated data cleaning. It filters out invalid entries before computing per-category counts and percentage shares, ensuring the final statistics and chart images reflect only valid classification data.

Chart types generated for classification distribution reporting include high-resolution combo charts like bar graphs with dual-axis share curves, alongside multi-figure reports combining pie charts and horizontal bar graphs with direct labeling. These visuals are produced using matplotlib to present category counts and proportions.