Pandas使用TimeStamp索引对齐多个数据帧

Question

在过去的几天里,这一直是我生命中的祸根.我有许多Pandas Dataframes包含不规则频率的时间序列数据.我尝试将这些对齐到一个数据帧中.

下面是一些代码,具有代表性的dataframes, ,df1,df2和df3(其实我的n = 5,并希望得到一个解决方案,将所有的工作n>2):

# df1, df2, df3 are given at the bottom
import pandas as pd
import datetime

# I can align df1 to df2 easily
df1aligned, df2aligned = df1.align(df2)
# And then concatenate into a single dataframe
combined_1_n_2 = pd.concat([df1aligned, df2aligned], axis =1 )
# Since I don't know any better, I then try to align df3 to combined_1_n_2  manually:
combined_1_n_2.align(df3)
error: Reindexing only valid with uniquely valued Index objects

我知道为什么我得到这个错误,所以我摆脱了重复的索引,combined_1_n_2然后再试一次:

combined_1_n_2 = combined_1_n_2.groupby(combined_1_n_2.index).first()
combined_1_n_2.align(df3) # But stll get the same error
error: Reindexing only valid with uniquely valued Index objects

为什么我收到此错误？即使这有效,它也完全是手工和丑陋的.如何对齐> 2个时间序列并将它们组合在一个数据帧中？

数据:

df1 = pd.DataFrame( {'price' : [62.1250,62.2500,62.2375,61.9250,61.9125 ]}, 
                     index = [pd.DatetimeIndex([datetime.datetime.strptime(s, '%Y-%m-%d %H:%M:%S.%f')])[0] 
                     for s in ['2008-06-01 06:03:59.614000', '2008-06-01 06:03:59.692000', 
                     '2008-06-01 06:15:42.004000', '2008-06-01 06:15:42.083000','2008-06-01 06:17:01.654000' ] ])   

df2 = pd.DataFrame({'price': [241.0625, 241.5000, 241.3750, 241.2500, 241.3750 ]},
                    index = [pd.DatetimeIndex([datetime.datetime.strptime(s, '%Y-%m-%d %H:%M:%S.%f')])[0] 
                     for s in ['2008-06-01 06:13:34.524000', '2008-06-01 06:13:34.602000', 
                     '2008-06-01 06:15:05.399000', '2008-06-01 06:15:05.399000','2008-06-01 06:15:42.082000' ] ])   

df3 = pd.DataFrame({'price': [67.656, 67.875, 67.8125, 67.75, 67.6875 ]},
                    index = [pd.DatetimeIndex([datetime.datetime.strptime(s, '%Y-%m-%d %H:%M:%S.%f')])[0] 
                     for s in ['2008-06-01 06:03:52.281000', '2008-06-01 06:03:52.359000', 
                     '2008-06-01 06:13:34.848000', '2008-06-01 06:13:34.926000','2008-06-01 06:15:05.321000' ] ])   

Answer 1

您的具体错误是由于列名combined_1_n_2具有重复项(两列都将命名为"price").您可以重命名列,第二个对齐也可以.

一种替代方法是链接join运算符,该运算符合并索引上的帧,如下所示.

In [23]: df1.join(df2, how='outer', rsuffix='_1').join(df3, how='outer', rsuffix='_2')
Out[23]: 
                              price   price_1  price_2
2008-06-01 06:03:52.281000      NaN       NaN  67.6560
2008-06-01 06:03:52.359000      NaN       NaN  67.8750
2008-06-01 06:03:59.614000  62.1250       NaN      NaN
2008-06-01 06:03:59.692000  62.2500       NaN      NaN
2008-06-01 06:13:34.524000      NaN  241.0625      NaN
2008-06-01 06:13:34.602000      NaN  241.5000      NaN
2008-06-01 06:13:34.848000      NaN       NaN  67.8125
2008-06-01 06:13:34.926000      NaN       NaN  67.7500
2008-06-01 06:15:05.321000      NaN       NaN  67.6875
2008-06-01 06:15:05.399000      NaN  241.3750      NaN
2008-06-01 06:15:05.399000      NaN  241.2500      NaN
2008-06-01 06:15:42.004000  62.2375       NaN      NaN
2008-06-01 06:15:42.082000      NaN  241.3750      NaN
2008-06-01 06:15:42.083000  61.9250       NaN      NaN
2008-06-01 06:17:01.654000  61.9125       NaN      NaN