将两个时间序列与 Python 中日期时间索引的不同元素结合起来

Question

I have two time-series below. df1 has an index in a DateTime format which includes only date without time. df2 has a full datetime index, also in a DateTime format. In the full data, df1 is much shorter than df2 in terms of the number of rows.

As you can see, both datasets span from the 2nd to the 6th of April. df1, however, skips some dates, while in df2, all days are available. Note: in this example, only odd dates are skipped, but it is not the case in the full data.

df1

    value1
date            
2016-04-02  16
2016-04-04  76
2016-04-06  23

df2

    value2
DateTime    
2016-04-02 07:45:00 257.96
2016-04-02 07:50:00 317.58
2016-04-02 07:55:00 333.39
2016-04-03 08:15:00 449.96
2016-04-03 08:20:00 466.42
2016-04-03 08:25:00 498.56
2016-04-04 08:10:00 454.73
2016-04-04 08:15:00 472.45
2016-04-04 08:20:00 489.85
2016-04-05 07:30:00 169.54
2016-04-05 07:35:00 276.13
2016-04-05 07:40:00 293.70
2016-04-06 07:10:00 108.05
2016-04-06 07:15:00 179.21
2016-04-06 07:20:00 201.80

I want to combine the two datasets by index. df1 should controls which dates to be kept. The expected result is below.

    value2  value1
DateTime    
2016-04-02 07:45:00 257.96  16
2016-04-02 07:50:00 317.58  16
2016-04-02 07:55:00 333.39  16
2016-04-04 08:10:00 454.73  76
2016-04-04 08:15:00 472.45  76
2016-04-04 08:20:00 489.85  76
2016-04-06 07:10:00 108.05  23
2016-04-06 07:15:00 179.21  23
2016-04-06 07:20:00 201.80  23

This is my attempt.

result= pd.concat([df1, df1], axis=1, sort=True).dropna(how='all')

But the result is different to what I expect.

Answer 1

Here is possible create new helper column filled by datetimes without times with DatetimeIndex.normalize :

df2['date'] = df2.index.normalize()

Or if dates use DatetimeIndex.date :

df2['date'] = df2.index.date

And then use merge with default inner join:

result= df1.merge(df2, left_index=True, right_on='date')
print (result)
                     value1  value2       date
DateTime                                      
2016-04-02 07:45:00      16  257.96 2016-04-02
2016-04-02 07:50:00      16  317.58 2016-04-02
2016-04-02 07:55:00      16  333.39 2016-04-02
2016-04-04 08:10:00      76  454.73 2016-04-04
2016-04-04 08:15:00      76  472.45 2016-04-04
2016-04-04 08:20:00      76  489.85 2016-04-04
2016-04-06 07:10:00      23  108.05 2016-04-06
2016-04-06 07:15:00      23  179.21 2016-04-06
2016-04-06 07:20:00      23  201.80 2016-04-06

---

Or use merge_asof , but it merging by previous match values, so working same like above only if always match datetimes without times from df2 with date s from df1 :

result= pd.merge_asof(df2, df1, left_index=True, right_index=True)
print (result)
                     value2  value1
DateTime                           
2016-04-02 07:45:00  257.96      16
2016-04-02 07:50:00  317.58      16
2016-04-02 07:55:00  333.39      16
2016-04-03 08:15:00  449.96      16
2016-04-03 08:20:00  466.42      16
2016-04-03 08:25:00  498.56      16
2016-04-04 08:10:00  454.73      76
2016-04-04 08:15:00  472.45      76
2016-04-04 08:20:00  489.85      76
2016-04-05 07:30:00  169.54      76
2016-04-05 07:35:00  276.13      76
2016-04-05 07:40:00  293.70      76
2016-04-06 07:10:00  108.05      23
2016-04-06 07:15:00  179.21      23
2016-04-06 07:20:00  201.80      23