如何基于组添加时间序列列

将列转换'Prod Date'为日期时间对数据帧进行排序'Prod Date'，'PL Seq'因此'df'与加入的顺序相同time_seq。答案的主要部分是创建一个包含和的DateRange列表.groupby.apply.groupby和Prod Date第一个元素'PL Seq'df.groupby(['Prod Date', df['PL Seq'].str[0]]).apply(lambda x: (pd.date_range(start=x.values[0] + pd.Timedelta(hours=8), periods=len(x), freq='s')).time)到目前为止，添加 8 小时的 Timedelta，以获得08:00:00对于每个组，使用 x 中的第一个值start：x.values[0]的数量periods是len[x]是freq，'s'几秒钟。这将创建一个DateRange，从中提取时间.time测试于python 3.10,pandas 1.4.3import pandas as pd# setup test dataframedata = {'MODELCHASS': ['M742-021167', 'M359-020535', 'M742-022095', 'M220-001083', 'M742-022390', 'M907-004747', 'M934-005904'],        'Prod Date': [20200917, 20200917, 20200917, 20200918, 20200918, 20200918, 20200918],        'PL Seq': ['G0005', 'G0010', 'G0015', 'G0400', 'G0405', 'H0090', 'H0095']}df = pd.DataFrame(data)# convert Prod Date to a datetime columndf['Prod Date'] = pd.to_datetime(df['Prod Date'], format='%Y%m%d')# sort the dataframe by values so the order will correspond to the groupby orderdf = df.sort_values(['Prod Date', 'PL Seq']).reset_index(drop=True)# groupby Prod Date and the first character of PL Seq# create a DateRange sequence for each group# reshape the dataframetime_seq = (df.groupby(['Prod Date', df['PL Seq'].str[0]])['Prod Date']            .apply(lambda x: (pd.date_range(start=x.values[0] + pd.Timedelta(hours=8), periods=len(x), freq='s')).time)            .reset_index(name='time_seq')            .explode('time_seq', ignore_index=True))# join the time_seq column to dfdf_new = df.join(time_seq.time_seq)# display(df_new)    MODELCHASS  Prod Date PL Seq  time_seq0  M742-021167 2020-09-17  G0005  08:00:001  M359-020535 2020-09-17  G0010  08:00:012  M742-022095 2020-09-17  G0015  08:00:023  M220-001083 2020-09-18  G0400  08:00:004  M742-022390 2020-09-18  G0405  08:00:015  M907-004747 2020-09-18  H0090  08:00:006  M934-005904 2020-09-18  H0095  08:00:01

如何基于组添加时间序列列

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