{
    "action": "final",
    "content": "## Distinct Values by Categorical Column\n\nI've analyzed all 44 columns in the dataset. Here's the breakdown of distinct values for each categorical column:\n\n### Survey Structure Columns\n- **Question**: 16 distinct values\n- **Response**: 65 distinct values\n- **question_number**: 15 distinct values\n- **question_verbatim**: 15 distinct values\n- **response_verbatim**: 65 distinct values\n\n### Demographic Breakdown Columns (Percentage Values)\n- **Overall**: 104 distinct values\n- **Gender: Male**: 101 distinct values\n- **Gender: Female**: 101 distinct values\n- **Age: 18–29**: 103 distinct values\n- **Age: 30–44**: 104 distinct values\n- **Age: 45–59**: 102 distinct values\n- **Age: 60+**: 100 distinct values\n\n### Income Segments\n- **Income: Under $25K**: 101 distinct values\n- **Income: $25K–$50K**: 101 distinct values\n- **Income: $50K–$75K**: 99 distinct values\n- **Income: $75K–$100K**: 97 distinct values\n- **Income: $100K+**: 101 distinct values\n\n### Education Levels\n- **Education: Less than HS**: 86 distinct values\n- **Education: High school**: 102 distinct values\n- **Education: Some college**: 104 distinct values\n- **Education: Bachelor+**: 104 distinct values\n\n### Employment Status\n- **Employment: Full-time**: 105 distinct values (all rows)\n- **Employment: Part-time**: 102 distinct values\n\n### Race/Ethnicity\n- **Race: White, Non-Hispanic**: 103 distinct values\n- **Race: Black, Non-Hispanic**: 99 distinct values\n- **Race: Hispanic**: 104 distinct values\n- **Race: Other/2+, Non-Hispanic**: 101 distinct values\n\n### Geographic Regions\n- **Region: Northeast**: 102 distinct values\n- **Region: Midwest**: 105 distinct values (all rows)\n- **Region: South**: 100 distinct values\n- **Region: West**: 102 distinct values\n\n### Occupation Categories\n- **Occupation: Software & IT**: 96 distinct values\n- **Occupation: Managers**: 103 distinct values\n- **Occupation: Business, finance & legal**: 103 distinct values\n- **Occupation: Education & social services**: 94 distinct values\n- **Occupation: Engineering & science**: 90 distinct values\n- **Occupation: Healthcare**: 96 distinct values\n- **Occupation: Sales, office/admin & creative**: 104 distinct values\n- **Occupation: Manual & in-person services**: 101 distinct values\n- **Occupation: Other (please specify)**: 87 distinct values\n\n### Metadata Columns\n- **_ingestion_timestamp**: 1 distinct value\n- **_batch_id**: 1 distinct value\n- **_source_file**: 1 distinct value\n- **_source_system**: 1 distinct value\n\n### Key Observations\n\n1. **High cardinality in demographic columns**: Most demographic breakdown columns have 96–105 distinct values, approaching the total row count (105). This indicates these columns contain percentage values with high precision rather than simple categories.\n\n2. **Survey structure**: The dataset contains 16 distinct questions with 65 distinct response options across all questions.\n\n3. **Metadata uniformity**: All metadata columns (_ingestion_timestamp, _batch_id, _source_file, _source_system) have only 1 distinct value, indicating this is a single-batch import from one source.\n\n4. **Occupation variation**: Occupation categories show the widest range in distinct values (87–104), with \"Engineering & science\" having the fewest (90) and \"Sales, office/admin & creative\" having the most (104)."
}