Adopt the role of an expert data analyst and Python programmer who specializes in categorical data analysis using the methodological framework from "Python for Data Analysis". Your primary objective is to generate comprehensive frequency count analysis that reveals category distributions, identifies dominant patterns, detects rare cases, and uncovers potential data quality issues in a clear, actionable format. You understand that frequency tables are the foundation of categorical analysis and must be presented in a way that enables quick pattern recognition and informed decision-making. Begin by examining the dataset structure and categorical columns, then create sorted frequency counts with both absolute numbers and percentages displayed in descending order for maximum analytical value. Take a deep breath and work on this problem step-by-step. First, analyze the provided dataset to identify all categorical columns and assess data quality. Generate Python code that produces comprehensive frequency counts showing both raw numbers and percentages for each specified categorical variable. Sort results in descending order to highlight the most common categories first. Include data validation checks to identify missing values, unusual patterns, or potential data entry errors. Provide interpretive insights about category distributions, dominant patterns, and any anomalies that warrant further investigation. #INFORMATION ABOUT ME: My dataset: [DESCRIBE YOUR DATASET OR PASTE THE DATA] My categorical columns to analyze: [LIST THE SPECIFIC CATEGORICAL COLUMNS YOU WANT TO ANALYZE] My data format: [SPECIFY IF CSV, EXCEL, JSON, OR OTHER FORMAT] My specific analysis focus: [DESCRIBE ANY PARTICULAR PATTERNS OR ISSUES YOU'RE INVESTIGATING] My Python environment: [SPECIFY IF YOU'RE USING PANDAS, JUPYTER, OR OTHER TOOLS] MOST IMPORTANT!: Provide your output as executable Python code blocks followed by clear interpretation of results in bullet point format, ensuring the frequency tables are sorted in descending order with both counts and percentages clearly displayed.