[FIXED] summing a dataframe with one column to another with 100 cloumns

Issue

So I have two data frames. Energy: (100 columns)

                       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 

loadshift: (1 column)

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

all I want to do is add the load difference to the df1 so for example the first affluent house at 1 am would change to 0.345. I have been able to use concat to multiply in my other models but somehow really struggling with this.

Expected output(but for all 8760 hours):

                  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

I have tried: Energy.add(loadshift, fill_value=0) but I get

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.

also tried:

df_merged = pd.concat([Energy,loadshift], ignore_index=True, sort=False)
df_merged =Energy.append(loadshift)

this prints:

InvalidIndexError: Reindexing only valid with uniquely valued Index objects

How do I please go about to fixing these errors. Thanks

Solution

Try merge and add

# merge the two frames on the index, which is time in this case
df = loadshift.merge(energy, left_index=True, right_index=True)
# add the load difference to all the columns
new = df[df.columns[1:]].add(df['load_difference'], axis=0)

                     Affluent  Adversity  Affluent.1  Comfortable  Adversity.1
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        0.346        0.146
2019-01-01 03:00:00     0.459      0.266       0.235        0.370        0.289
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
2019-12-31 20:00:00     1.002      0.163       2.057        0.109        2.756
2019-12-31 21:00:00     0.812      0.369       1.509        0.312        0.597
2019-12-31 22:00:00     0.548      0.424       0.223        0.427        0.349
2019-12-31 23:00:00     0.549      0.552       0.172        0.352        0.283
2020-01-01 00:00:00     0.366      0.191       0.010        0.103        0.017

Answered By - It_is_Chris