Categorical Variable Profiler

Pro v1.0.0 1 view

Automated categorical variable analysis for datasets. Generates frequency tables, percentage breakdowns, bar chart visualizations, rare category identification, cross-tabulations between categorical pairs, and chi-square association tests.

What You Get

Saves hours of manual Excel PivotTable work by automatically profiling categorical variables, identifying patterns and associations, checking data quality, and providing actionable insights with statistical rigor.

The Problem

Data analysts spend significant time manually creating frequency tables, pivot tables, and cross-tabulations in Excel or SQL to understand categorical variable distributions and relationships. This manual work is tedious, error-prone, and often misses important statistical associations or data quality issues. Analysts need a comprehensive, automated solution that profiles categorical data, detects associations, and provides statistically valid insights quickly.

The Solution

This skill automates the complete categorical variable profiling workflow using Python's pandas, scipy, and seaborn libraries. It loads your dataset, identifies categorical columns, generates comprehensive frequency tables with percentages and cumulative distributions, creates visualizations, flags rare categories, checks for data quality issues (duplicates, missing values, inconsistent formatting), produces cross-tabulations between categorical pairs, runs chi-square tests with proper multiple testing corrections, calculates effect sizes (Cramér's V), and synthesizes findings into an actionable executive summary. The analysis is performed interactively with progress shown at each step, allowing you to guide the process while receiving immediate insights.

How It Works

  1. 1 Load dataset and identify categorical variables based on data types and cardinality
  2. 2 Generate univariate profiles with frequency tables, percentages, visualizations, and rare category identification for each variable
  3. 3 Perform data quality checks detecting missing values, duplicates, inconsistent casing, whitespace issues, and potential data entry errors
  4. 4 Create cross-tabulations showing relationships between categorical variable pairs with contingency tables, row/column percentages, and heatmaps
  5. 5 Run chi-square tests of independence with assumption validation, effect size calculations (Cramér's V), and multiple testing corrections
  6. 6 Synthesize findings into executive summary with key insights, significant associations, data quality issues, and actionable recommendations

What You'll Need

  • Dataset file in CSV, Excel, Parquet, or TSV format
  • At least one categorical variable in the dataset
  • File path or reference to uploaded file

Get This Skill

Requires Pro subscription ($9/month)

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Examples

Customer Satisfaction Regional Analysis

Comprehensive analysis of 30-customer survey dataset with Region, Age_Group, Satisfaction, and Product_Category variables. Demonstrates complete workflow including frequency tables, chi-square tests with multiple testing correction, effect size calculations (Cramér's V), and visualization generation. Identifies critical West region dissatisfaction issue (57% vs 0% in South) and strong statistical associations between variables.

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Focused Regional Satisfaction Association

Targeted analysis examining the relationship between specific categorical variables (Region and Satisfaction). Demonstrates the skill's flexibility in handling user-directed analysis requests, performing chi-square independence tests with effect size interpretation, and delivering focused insights with clear regional performance breakdown and urgent recommendations for business action.

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Rare Category Detection and Data Quality Check

Analysis focused on identifying rare categories (below 1% threshold) and comprehensive data quality assessment. Demonstrates the skill's ability to flag small categories, detect data quality issues, and provide exploratory insights with appropriate statistical caveats for small sample sizes. Shows proper handling of chi-square assumption violations with transparent reporting.

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