我正在尋找基于另一個(gè)只有三列的小得多的數(shù)據(jù)框來過濾一個(gè)大數(shù)據(jù)框（數(shù)百萬行）：ID、開始、結(jié)束。以下是我放在一起的內(nèi)容（有效），但似乎groupby()或np.where可能更快。設(shè)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(guān)于更優(yōu)雅/更快的解決方案的任何想法？