我有一个如下所示的 csv 文件
327,2018-02-12 23:30:18.255810+00:00,Pur,10.11.100.1,WSE,8.0,23.0,6.5,0.0,,,,,,,,
328,2018-02-12 23:30:22.718605+00:00,Bol,10.11.100.1,DEF,8.0,23.0,11.41,0.0,,,,,,,,
333,2018-02-13 00:00:17.886487+00:00,Cal,10.11.100.1,WSE,9.0,23.0,10.5,0.0,,,,,,,,
334,2018-02-13 00:00:21.948083+00:00,Moe,10.11.100.1,CFG,9.0,23.0,21.5,0.0,,,,,,,,
436,2018-02-15 11:00:11.137740+00:00,Cad,10.11.100.1,MOD,5.0,24.0,3.17,0.0,,,,,,,,
437,2018-02-15 11:27:20.994247+00:00,Ric,10.11.100.1,DEF,7.0,24.0,9.5,0.0,,,,,,,,
877,2018-02-17 01:34:10.662735+00:00,Pit,10.4.100.1,CFD,6.0,3.0,37.23,0.0,,,,,,,,
878,2018-02-20 00:04:39.855528+00:00,Bol,10.4.100.1,WSE,9.0,3.0,55.42,0.0,,,,,,,,
日期范围是2018-02-02到2018-04-13
我试过做这样的事情，如这里提到的Pandas Reindex to Fill Missing Dates, or Better Method to Fill?
df = pd.read_csv(file, parse_dates=["date"])
df.set_index("date", inplace=True)
df.index = pd.to_datetime(df.index,format='%Y-%m-%d %H:%M:%S.%f')
d2 = pd.DataFrame(index=pd.date_range('2018-02-02','2018-04-13'))
print(df.join(d2,how='right'))
但这似乎不起作用。我仍然缺少一些日期。填写缺少的日期并分配0给其关联值的正确方法是什么？
您只需要将索引转换为普通日期即可使您自己的解决方案起作用：
df = pd.read_csv(file, parse_dates=['date'])
df.set_index('date', inplace=True)
df.index = df.index.date
d2 = pd.DataFrame(index=pd.date_range('2018-02-12','2018-02-20'))
print(df.join(d2, how='right').fillna(0))
它应该给
2018-02-12 327.0 Pur 10.11.100.1 WSE 8.0 23.0 6.50 0.0
2018-02-12 328.0 Bol 10.11.100.1 DEF 8.0 23.0 11.41 0.0
2018-02-13 333.0 Cal 10.11.100.1 WSE 9.0 23.0 10.50 0.0
2018-02-13 334.0 Moe 10.11.100.1 CFG 9.0 23.0 21.50 0.0
2018-02-14 0.0 0 0 0 0.0 0.0 0.00 0.0
2018-02-15 436.0 Cad 10.11.100.1 MOD 5.0 24.0 3.17 0.0
2018-02-15 437.0 Ric 10.11.100.1 DEF 7.0 24.0 9.50 0.0
2018-02-16 0.0 0 0 0 0.0 0.0 0.00 0.0
2018-02-17 877.0 Pit 10.4.100.1 CFD 6.0 3.0 37.23 0.0
2018-02-18 0.0 0 0 0 0.0 0.0 0.00 0.0
2018-02-19 0.0 0 0 0 0.0 0.0 0.00 0.0
2018-02-20 878.0 Bol 10.4.100.1 WSE 9.0 3.0 55.42 0.0
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我来说两句