code: def mau(dataframe, target_day, past_days=28, future_days=10, date_format="%Y%m%d"):
      
      target_day_date = datetime.strptime(target_day, date_format)
  
      # Compute activity over `past_days` days leading up to target_day
      min_activity_date = target_day_date - timedelta(past_days)
      min_activity = unix_time_nanos(min_activity_date)
      max_activity = unix_time_nanos(target_day_date + timedelta(1))
      act_col = dataframe.activityTimestamp
  
      min_submission = datetime.strftime(min_activity_date, date_format)
      max_submission_date = target_day_date + timedelta(future_days)
      max_submission = datetime.strftime(max_submission_date, date_format)
      sub_col = dataframe.submission_date_s3
  
      filtered = filter_date_range(dataframe, act_col, min_activity, max_activity,
                                   sub_col, min_submission, max_submission)
      return count_distinct_clientids(filtered)
docs: Compute Monthly Active Users (MAU) from the Executive Summary dataset.
      See https://bugzilla.mozilla.org/show_bug.cgi?id=1240849