category-filtering-and-difficulty-analysis

Cross-analyze Excel category distributions by building crosstabs and stacked bar charts to compare category frequencies across a specified grouping column. The Skill maps raw values into higher-level groups and visualizes subgroup comparisons.

Text difficulty scoring evaluates content quality by computing a comprehensive score using text length, keyword and regex signals, and numeric existence. It maps the resulting score against tier thresholds to output high, medium, or low difficulty levels.

Yes, you can map raw Excel category values into higher-level custom groups for analysis. The Skill applies category mapping logic to convert raw values, then generates distribution counts and share visualizations using bar and pie charts.

Yes, this Skill requires Python-based data loading to import Excel datasets for category aggregation. It uses Python to execute value_counts and crosstab statistical counting, along with category mapping logic for multidimensional office analytics.

The best way to visualize multi-class distribution statistics from an Excel file is to generate bar and pie charts after aggregating custom categories. This Skill produces share visualizations and stacked bar charts to compare distributions across subgroups.

Regex and keyword signals provide multidimensional feature extraction for text difficulty evaluation in Excel datasets. Combining these signals with text length and numeric existence generates a comprehensive score that accurately tiers content into high, medium, or low difficulty.