{
    "action": "final",
    "content": "The query result above (step-0) already shows the full value distribution for every main categorical column. The 8,500 rows each have one value per column; the rendered table lists every value and its share.\n\n**Most common value per column**\n- **primary_bedtime_app**: TikTok / Reels is the most common at 2,198 rows (25.9%). It is followed by YouTube (22.7%) and Instagram / Reddit (19.2%). The least common is News / Reading (7.0%), so the six apps are fairly spread out, with short-form video and YouTube together making up about 49% of users.\n- **occupation_type**: Corporate 9-to-5 leads at 2,838 rows (33.4%), then Remote Tech (25.2%) and Student (19.1%). Healthcare / Shift Worker (11.7%) and Freelance / Creative (10.6%) are the smallest groups.\n- **sleep_debt_category**: Moderate Debt dominates at 4,462 rows (52.5%). Mild Deficit is 23.6%, Optimal Recovery 16.3%, and Severe Sleep Debt 7.6%. Only about 16% of users are in the optimal-recovery category, and roughly 60% are at Moderate Debt or worse.\n- **chronotype**: Intermediate is most common at 3,880 rows (45.6%), then Night Owl (28.9%) and Morning Lark (25.5%).\n- **gender**: Female is 51.1% (4,347 rows) and Male is 45.9% (3,905 rows). Non-Binary is only 2.9% (248 rows), so any subgroup analysis on it will rest on a small sample.\n- **blue_light_filter_active**: Roughly balanced, with 0 (filter off) at 53.2% (4,524 rows) and 1 (filter on) at 46.8% (3,976 rows).\n\n**Takeaways**\n- No column is dominated by a single value except sleep_debt_category, where Moderate Debt is a majority.\n- Gender and occupation groups are uneven, so comparisons across Non-Binary users or Freelance and Healthcare workers have far fewer rows behind them.\n\nThis came from a single frequency query over the six categorical columns (23 value rows in total)."
}