对float64与object和int64列的不同条件的时间序列进行上采样 [英] Upsampling a time-series with different conditions for float64 vs object and int64 columns

问题描述

我有一个与此类似的df:

print(df)
                        A     B  C
DATE_TIME                         
2016-10-08 13:57:00   in   5.61  0
2016-10-08 14:02:00   in   8.05  0
2016-10-08 14:07:00  out   7.92  0
2016-10-08 14:12:00   in   7.98  1
2016-10-08 14:17:00  out   8.18  0
2016-10-08 14:22:00  out   7.59  0

print (df.dtypes)
A     object
B    float64
C      int64
dtype: object

我想将此df重新采样为1S频率，以便可以将其与另一个df连接起来. 我无法解决的问题是对于object和int64列，我希望对新创建的时间行重复相同的值，这可以通过以下功能完成:

I want to resample this df to a 1S frecuency, so that I can concatenate it with another df. The problem I can't solve is that for the columns type object and int64 I want the same value repeated for the newly created time rows, which could be done by this function:

df=df.resample('S', fill_method='pad')

而对于float64列，我正在寻找:

whereas for the float64 columns I am looking for this:

df=df.interpolate()

我曾考虑过要使用IF语句，但是我也想，我首先必须在插值步骤之前执行重采样步骤.当我仅按df=df.resample('S')重新采样时，我便可以进行插值，该方法适用于float64列，但不适用于object和Int64列. 有人可以帮我吗?谢谢.

I thought about applying an IF statement, but I also figuered that I first have to do the resample step before the interpolation step. When I resample just by df=df.resample('S') I can interpolate afterwards, which works for the float64 columns but not for the object and Int64 ones. Could anybody help me please? Thanks.

推荐答案

以下是使用reindex的方法:

index = pd.date_range(df.index[0], df.index[-1], freq="s")
df2 = df.reindex(index)
for col, s in df2.iteritems():
    if s.dtype == float:
        s.interpolate(inplace=True)
    else:
        s.ffill(inplace=True)

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