我有两个数据文件,它们都有不同的日期时间。
如下文所示,第一个“日期”为2013-10-14至2015-11-25,第二个“日期”为2014-01-01至2015-11-27。
如果我想把日期从2013-10-14到2015-11-27,并填补空白作为np.nan,我必须在代码中做什么?
如果你知道怎么做或者有什么想法,请告诉我。
dvv : Date
2013-10-14 -0.038875
2013-10-15 -0.038875
2013-10-16 -0.038875
2013-10-17 -0.038875
2013-10-18 -0.038875
2015-11-21 0.081939
2015-11-22 -0.097986
2015-11-23 -0.096201
2015-11-24 -0.033913
2015-11-25 -0.050553
Name: dvv, Length: 773, dtype: float64
Stations Sensor EL GL Pressure Temp EC Barometa
Date
2014-01-01 JRee3 S11 NaN NaN NaN NaN NaN NaN
2014-01-02 JRee3 S11 NaN NaN NaN NaN NaN NaN
2014-01-02 JRee3 S11 NaN NaN NaN NaN NaN NaN
2014-01-04 JRee3 S11 NaN NaN NaN NaN NaN NaN
2014-01-05 JRee3 S11 NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ...
2015-11-23 JRee3 S11 213.46 202.21 99.83 14.22 105.0 1008.13
2015-11-24 JRee3 S11 213.36 202.31 99.73 14.22 105.0 1008.36
2015-11-25 JRee3 S11 213.34 202.33 99.71 14.22 105.0 1004.40
2015-11-26 JRee3 S11 213.30 202.37 99.67 14.22 105.0 1003.13
2015-11-27 JRee3 S11 213.24 202.44 99.61 14.21 105.0 1011.00
[696 rows x 8 columns]发布于 2021-12-19 10:02:48
假设日期中没有缺少值,那么您可以简单地利用pandas.date_range和外部联接。
玩具示例如下:
import pandas as pd
dates1 = pd.date_range('2013-10-14', '2015-11-25', freq='D')
dates2 = pd.date_range('2014-01-01', '2015-11-27', freq='D')
df1 = pd.DataFrame(data=[1]*len(dates1), index=dates1, columns=['var'])
df2 = pd.DataFrame(data=[2]*len(dates2), index=dates2, columns=['var'])
df1.merge(df2, left_index=True, right_index=True, how='outer')发布于 2021-12-19 09:49:27
您可以以这种方式生成新日期(用足够的数量替换期间):
days = pd.date_range('14/10/2013', periods=365, freq='D')您将得到这样的内容,您可以将其添加到您的数据文件中:
DatetimeIndex(['2013-10-14', '2013-10-15', '2013-10-16', '2013-10-17',
'2013-10-18', '2013-10-19', '2013-10-20', '2013-10-21',
'2013-10-22', '2013-10-23',
...
'2014-10-04', '2014-10-05', '2014-10-06', '2014-10-07',
'2014-10-08', '2014-10-09', '2014-10-10', '2014-10-11',
'2014-10-12', '2014-10-13'],
dtype='datetime64[ns]', length=365, freq='D')https://stackoverflow.com/questions/70410027
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