Pandas : merge on date and hour from datetime index

Question

I have two data frames like following, data frame A has datetime even with minutes, data frame B only has hour.

df:A

dataDate             original    
2018-09-30 11:20:00     3
2018-10-01 12:40:00     10
2018-10-02 07:00:00     5
2018-10-27 12:50:00     5
2018-11-28 19:45:00     7

df:B
dataDate             count    
2018-09-30 10:00:00     300
2018-10-01 12:00:00     50
2018-10-02 07:00:00     120
2018-10-27 12:00:00     234
2018-11-28 19:05:00     714

I like to merge the two on the basis of hour date and hour, so that now in dataframe A should have all the rows filled on the basis of merge on date and hour

I can try to do it via

 A['date'] = A.dataDate.date
 B['date'] = B.dataDate.date

 A['hour'] = A.dataDate.hour
 B['hour'] = B.dataDate.hour

and then merge

 merge_df = pd.merge(A,B, how='left', left_on=['date', 'hour'], 
           right_on=['date', 'hour'])

but its a very long process, Is their an efficient way to perform the same operation with the help of pandas time series or date functionality?

Answer 1

Use map if need append only one column from B to A with floor for set minute s and second s if exist to 0 :

d = dict(zip(B.dataDate.dt.floor('H'), B['count']))
A['count'] = A.dataDate.dt.floor('H').map(d)
print (A)
             dataDate  original  count
0 2018-09-30 11:20:00         3    NaN
1 2018-10-01 12:40:00        10   50.0
2 2018-10-02 07:00:00         5  120.0
3 2018-10-27 12:50:00         5  234.0
4 2018-11-28 19:45:00         7  714.0

For general solution use DataFrame.join :

A.index = A.dataDate.dt.floor('H')
B.index = B.dataDate.dt.floor('H')

A = A.join(B, lsuffix='_left')
print (A)
                          dataDate_left  original            dataDate  count
dataDate                                                                    
2018-09-30 11:00:00 2018-09-30 11:20:00         3                 NaT    NaN
2018-10-01 12:00:00 2018-10-01 12:40:00        10 2018-10-01 12:00:00   50.0
2018-10-02 07:00:00 2018-10-02 07:00:00         5 2018-10-02 07:00:00  120.0
2018-10-27 12:00:00 2018-10-27 12:50:00         5 2018-10-27 12:00:00  234.0
2018-11-28 19:00:00 2018-11-28 19:45:00         7 2018-11-28 19:05:00  714.0