WITH lagged AS (
      SELECT 
          *, 
          LAG(event_time) OVER (PARTITION BY user_id ORDER BY event_time) AS prev_time
      FROM events
  ),
  sessions AS (
      SELECT 
          *, 
          SUM(COALESCE((date_diff('minute', prev_time, event_time) > 30)::INT, 1)) 
              OVER (PARTITION BY user_id ORDER BY event_time) AS session_id
      FROM lagged
  )
  SELECT
      user_id,
      session_id,
      MIN(event_time) AS session_start,
      MAX(event_time) AS session_end,
      COUNT(*) AS event_count
  FROM sessions
  GROUP BY ALL
  ORDER BY user_id, session_start;

  result = (
      df.sort(["user_id", "event_time"])
      .with_columns(
          session_id=(
              pl.when(pl.col("event_time").diff().is_null())
              .then(1)
              .when(pl.col("event_time").diff().dt.total_minutes() > 30)
              .then(1)
              .otherwise(0)
              .cum_sum()
              .over("user_id")
          )
      )
      .group_by(["user_id", "session_id"])
      .agg(
          session_start=pl.col("event_time").min(),
          session_end=pl.col("event_time").max(),
          event_count=pl.col("event_time").count(),
      )
      .sort(["user_id", "session_start"])
  )

SQL has been around since the dawn of databases. I am happy to see a trend away from pandas.

Performance is definitely one of them, but it also has inconsistent and duplicated methods, inconsistent defaults (e.g. some methods are inplace by default), copy by reference issues, I could go on.

It was an early winner in an extremely popular language. That's really the main thing going for it, but alternatives have been a long time coming.