我正在尋找基于另一個只有三列的小得多的數據框來過濾一個大數據框（數百萬行）：ID、開始、結束。以下是我放在一起的內容（有效），但似乎groupby()或np.where可能更快。設置：import pandas as pdimport iocsv = io.StringIO(u'''time    id  num2018-01-01 00:00:00 A   12018-01-01 01:00:00 A   22018-01-01 02:00:00 A   32018-01-01 03:00:00 A   42018-01-01 04:00:00 A   52018-01-01 05:00:00 A   62018-01-01 06:00:00 A   62018-01-03 07:00:00 B   102018-01-03 08:00:00 B   112018-01-03 09:00:00 B   122018-01-03 10:00:00 B   132018-01-03 11:00:00 B   142018-01-03 12:00:00 B   152018-01-03 13:00:00 B   162018-05-29 23:00:00 C   1112018-05-30 00:00:00 C   1222018-05-30 01:00:00 C   1332018-05-30 02:00:00 C   1442018-05-30 03:00:00 C   155''')df = pd.read_csv(csv, sep = '\t')df['time'] = pd.to_datetime(df['time'])csv_filter = io.StringIO(u'''id  start   endA   2018-01-01 01:00:00 2018-01-01 02:00:00B   2018-01-03 09:00:00 2018-01-03 12:00:00C   2018-05-30 00:00:00 2018-05-30 08:00:00''')df_filter = pd.read_csv(csv_filter, sep = '\t')df_filter['start'] = pd.to_datetime(df_filter['start'])df_filter['end'] = pd.to_datetime(df_filter['end'])工作代碼df = pd.merge_asof(df, df_filter, left_on = 'time', right_on = 'start', by = 'id').dropna(subset = ['start']).drop(['start','end'], axis = 1)df = pd.merge_asof(df, df_filter, left_on = 'time', right_on = 'end', by = 'id', direction = 'forward').dropna(subset = ['end']).drop(['start','end'], axis = 1)輸出                  time id  num0  2018-01-01 01:00:00  A    21  2018-01-01 02:00:00  A    36  2018-01-03 09:00:00  B   127  2018-01-03 10:00:00  B   138  2018-01-03 11:00:00  B   149  2018-01-03 12:00:00  B   1511 2018-05-30 00:00:00  C  12212 2018-05-30 01:00:00  C  13313 2018-05-30 02:00:00  C  14414 2018-05-30 03:00:00  C  155關于更優雅/更快的解決方案的任何想法？