Find Users with Persistent Behavior Patterns solution and explanation

# Write your MySQL query statement below
WITH
    daily_counts AS (
        -- Step 1: Filter user dates with exactly one record per day (meeting the requirement of "exactly one action per day")
        SELECT
            user_id,
            action_date,
            action,
            COUNT(*) OVER (PARTITION BY user_id, action_date) AS cnt
        FROM activity
    ),
    filtered_activity AS (
        -- Step 2: Filter out data with multiple actions on the same day
        SELECT user_id, action_date, action
        FROM daily_counts
        WHERE cnt = 1
    ),
    streak_groups AS (
        -- Step 3: Group consecutive dates using the method of subtracting row number from date
        SELECT
            user_id,
            action,
            action_date,
            DATE_SUB(
                action_date,
                INTERVAL ROW_NUMBER() OVER (
                    PARTITION BY user_id, action
                    ORDER BY action_date
                ) DAY
            ) AS grp
        FROM filtered_activity
    ),
    streak_summary AS (
        -- Step 4: Calculate the length of each consecutive segment and only keep records with length >= 5
        SELECT
            user_id,
            action,
            COUNT(*) AS streak_length,
            MIN(action_date) AS start_date,
            MAX(action_date) AS end_date,
            -- Sort different streaks for each user to facilitate getting the maximum value later
            ROW_NUMBER() OVER (
                PARTITION BY user_id
                ORDER BY COUNT(*) DESC
            ) AS rnk
        FROM streak_groups
        GROUP BY user_id, action, grp
        HAVING streak_length >= 5
    )
-- Step 5: Extract the longest record for each qualified user and sort
SELECT user_id, action, streak_length, start_date, end_date
FROM streak_summary
WHERE rnk = 1
ORDER BY streak_length DESC, user_id ASC;

import pandas as pd

def find_behaviorally_stable_users(activity: pd.DataFrame) -> pd.DataFrame:
    activity['action_date'] = pd.to_datetime(activity['action_date'])

    # Filter users with only a single action per day
    df = activity.assign(cnt=activity.groupby(['user_id', 'action_date'])['action'].transform('count'))
    df = df[df['cnt'] == 1].sort_values(['user_id', 'action', 'action_date'])

    # Identify consecutive intervals
    df['rn'] = df.groupby(['user_id', 'action'])['action_date'].rank(method='first')
    df['grp'] = df['action_date'] - pd.to_timedelta(df['rn'], unit='D')

    # Aggregate streaks
    streaks = df.groupby(['user_id', 'action', 'grp']).agg(
        streak_length=('action_date', 'count'),
        start_date=('action_date', 'min'),
        end_date=('action_date', 'max')
    ).reset_index()

    # Filter and get the longest streak for each user
    res = streaks[streaks['streak_length'] >= 5].sort_values(
        ['streak_length', 'user_id'], ascending=[False, True]
    )

    return res.groupby('user_id').head(1)[['user_id', 'action', 'streak_length', 'start_date', 'end_date']]