所以我有两个数据帧。能源：
Affluent Adversity Affluent Comfortable Adversity
Time
2019-01-01 01:00:00 0.254 0.244 0.155 0.215 0.274
2019-01-01 02:00:00 0.346 0.154 0.083 0.246 0.046
2019-01-01 03:00:00 0.309 0.116 0.085 0.220 0.139
2019-01-01 04:00:00 0.302 0.158 0.083 0.226 0.186
2019-01-01 05:00:00 0.181 0.171 0.096 0.246 0.051
... ... ... ... ... ...
2019-12-31 20:00:00 1.102 0.263 2.157 0.209 2.856
2019-12-31 21:00:00 0.712 0.269 1.409 0.212 0.497
2019-12-31 22:00:00 0.398 0.274 0.073 0.277 0.199
2019-12-31 23:00:00 0.449 0.452 0.072 0.252 0.183
2020-01-01 00:00:00 0.466 0.291 0.110 0.203 0.117
装载转移：
Time load_difference
2019-01-01 01:00:00 0.10
2019-01-01 02:00:00 0.10
2019-01-01 03:00:00 0.15
2019-01-01 04:00:00 0.10
2019-01-01 05:00:00 0.10
... ...
2019-12-31 20:00:00 -0.10
2019-12-31 21:00:00 0.10
2019-12-31 22:00:00 0.15
2019-12-31 23:00:00 0.10
2020-01-01 00:00:00 -0.10
我想做的就是把负荷差加到df1上，这样例如，凌晨1点的第一栋富裕的房子就会变成0.345。我已经能够在其他模型中使用concat进行乘法运算，但不知何故，我真的很难做到这一点。
预期输出(但全部8760小时(：
Affluent Adversity Affluent Comfortable Adversity
Time
2019-01-01 01:00:00 0.354 0.344 0.255 0.315 0.374
2019-01-01 02:00:00 0.446 0.254 0.183 0.446 0.146
2019-01-01 03:00:00 0.409 0.216 0.185 0.320 0.239
2019-01-01 04:00:00 0.402 0.258 0.183 0.326 0.286
2019-01-01 05:00:00 0.281 0.271 0.196 0.346 0.151
我尝试过：Energy.add(loadshift, fill_value=0)但我有
Concatenation operation is not implemented for NumPy arrays, use np.concatenate() instead. Please do not rely on this error; it may not be given on all Python implementations.
也尝试过：
df_merged = pd.concat([Energy,loadshift], ignore_index=True, sort=False)
df_merged =Energy.append(loadshift)
这个打印：
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
我该如何着手纠正这些错误呢。感谢
这里有一种方法，那就是使用添加然后更新
#to add two DF, both should have same number of columns
# so, we duplicate single column in loadshift(DF2) into as many columns as we #have in the energy df (DF), minus the datetime column.
#next we concat to add the datetime column to have a new df (DF3) that #matches the columns count with the energy (df) DF
df3=pd.concat([df2.iloc[:,0],
pd.concat(
[df2.iloc[:,1]]*(len(df.columns)-1), axis=1
)]
, axis=1 )
#update column names
df3.columns = df.columns
# add DF and DF3 values and then update the original energy (df ) columns
df.update(df.iloc[:,1:].add(df3.iloc[:,1:]))
df
Time Affluent Adversity Affluent.1 Comfortable Adversity.1
0 2019-01-01 01:00:00 0.354 0.344 0.255 0.315 0.374
1 2019-01-01 02:00:00 0.446 0.254 0.183 0.346 0.146
2 2019-01-01 03:00:00 0.459 0.266 0.235 0.370 0.289
3 2019-01-01 04:00:00 0.402 0.258 0.183 0.326 0.286
4 2019-01-01 05:00:00 0.281 0.271 0.196 0.346 0.151
5 2019-12-31 20:00:00 1.002 0.163 2.057 0.109 2.756
6 2019-12-31 21:00:00 0.812 0.369 1.509 0.312 0.597
7 2019-12-31 22:00:00 0.548 0.424 0.223 0.427 0.349
8 2019-12-31 23:00:00 0.549 0.552 0.172 0.352 0.283
9 2020-01-01 00:00:00 0.366 0.191 0.010 0.103 0.017