熊猫数据框添加缺少的时间戳行，然后向前填充以前的值
[英]Pandas Dataframe add missing timestamp row and then forward fill previous values
问题描述
I have a pandas dataframe as follows: 
我有一个熊猫数据框，如下所示：
Timestamp Player Rotated Lat Rotated Lon
2018-11-11 16:22:21.999993600 G -15.89769 84.714795
2018-11-11 16:22:21.999993600 W -15.897637 84.714784
2018-11-11 16:22:21.999993600 K -15.897617 84.714621
2018-11-11 16:22:21.999993600 Y -15.897638 84.714787
2018-11-11 16:22:22.099958400 K -15.897618 84.714623
2018-11-11 16:22:22.099958400 Y -15.897691 84.714796
2018-11-11 16:22:22.099958400 W -15.897619 84.714626
2018-11-11 16:22:22.200009600 Y -15.897693 84.714794
2018-11-11 16:22:22.200009600 G -15.897639 84.714788
2018-11-11 16:22:22.200009600 K -15.897693 84.714802
2018-11-11 16:22:22.299974400 W -15.897692 84.714796
2018-11-11 16:22:22.299974400 G -15.897622 84.714629
2018-11-11 16:22:22.299974400 Y -15.897639 84.714791
2018-11-11 16:22:22.299974400 K -15.897694 84.714799
2018-11-11 16:22:22.400025600 G -15.89764 84.714794
2018-11-11 16:22:22.400025600 K -15.897622 84.714632
2018-11-11 16:22:22.400025600 Y -15.897692 84.714804
2018-11-11 16:22:22.400025600 W -15.897623 84.714635
2018-11-11 16:22:22.499990400 Y -15.897692 84.714806
2018-11-11 16:22:22.499990400 W -15.897694 84.714802
2018-11-11 16:22:22.499990400 G -15.897641 84.714795
2018-11-11 16:22:22.499990400 K -15.897694 84.714808
If you notice, I have 4 players: G, W, K, Y. Therefore there should be 4 of each timestamp index. 如果您注意到，我有4个玩家：G，W，K，Y。因此每个时间戳记索引中应该有4个。 However, some Timestamps are missing. 但是，缺少一些时间戳。 How can I add all the missing timestamps and then forward fill the other values to get only those players who are not in a given timestamp? 如何添加所有缺少的时间戳，然后向前填充其他值，以仅获取不在给定时间戳中的那些球员？
For example, for timestamp 2018-11-11 16:22:22.099958400 , Player G is missing. 例如，对于时间戳2018-11-11 16:22:22.099958400 ，玩家G丢失了。 How can I fill for just that player? 我该如何填补这个球员？
Desired output (I have spaced the frame to make it more readable): 所需的输出（我已将框架隔开以使其更具可读性）：
Timestamp Player Rotated Lat Rotated Lon
2018-11-11 16:22:21.999993600 G -15.89769 84.714795
2018-11-11 16:22:21.999993600 W -15.897637 84.714784
2018-11-11 16:22:21.999993600 K -15.897617 84.714621
2018-11-11 16:22:21.999993600 Y -15.897638 84.714787
2018-11-11 16:22:22.099958400 K -15.897618 84.714623
2018-11-11 16:22:22.099958400 Y -15.897691 84.714796
2018-11-11 16:22:22.099958400 W -15.897619 84.714626
2018-11-11 16:22:22.099958400 G -15.89769 84.714795
2018-11-11 16:22:22.200009600 Y -15.897693 84.714794
2018-11-11 16:22:22.200009600 G -15.897639 84.714788
2018-11-11 16:22:22.200009600 K -15.897693 84.714802
2018-11-11 16:22:22.200009600 W -15.897619 84.714626
1 个解决方案
解决方案1
5 已采纳 2019-01-11 07:19:52
Use set_index with unstack for reshape, forward fill missing values and last reshape back by stack : 使用set_index与unstack的重塑，向前填补缺失值和最后重塑背部stack ：
df = df.set_index('Player', append=True).unstack().ffill().stack().reset_index(level=1)
print (df)
Player Rotated Lat Rotated Lon
Timestamp
2018-11-11 16:22:21.999993600 G -15.897690 84.714795
2018-11-11 16:22:21.999993600 K -15.897617 84.714621
2018-11-11 16:22:21.999993600 W -15.897637 84.714784
2018-11-11 16:22:21.999993600 Y -15.897638 84.714787
2018-11-11 16:22:22.099958400 G -15.897690 84.714795
2018-11-11 16:22:22.099958400 K -15.897618 84.714623
2018-11-11 16:22:22.099958400 W -15.897619 84.714626
2018-11-11 16:22:22.099958400 Y -15.897691 84.714796
2018-11-11 16:22:22.200009600 G -15.897639 84.714788
2018-11-11 16:22:22.200009600 K -15.897693 84.714802
2018-11-11 16:22:22.200009600 W -15.897619 84.714626
2018-11-11 16:22:22.200009600 Y -15.897693 84.714794
2018-11-11 16:22:22.299974400 G -15.897622 84.714629
2018-11-11 16:22:22.299974400 K -15.897694 84.714799
2018-11-11 16:22:22.299974400 W -15.897692 84.714796
2018-11-11 16:22:22.299974400 Y -15.897639 84.714791
2018-11-11 16:22:22.400025600 G -15.897640 84.714794
2018-11-11 16:22:22.400025600 K -15.897622 84.714632
2018-11-11 16:22:22.400025600 W -15.897623 84.714635
2018-11-11 16:22:22.400025600 Y -15.897692 84.714804
2018-11-11 16:22:22.499990400 G -15.897641 84.714795
2018-11-11 16:22:22.499990400 K -15.897694 84.714808
2018-11-11 16:22:22.499990400 W -15.897694 84.714802
2018-11-11 16:22:22.499990400 Y -15.897692 84.714806
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