在过去的几天里，这一直是我生活中的祸根。我有许多 Pandas Dataframes，其中包含频率不规则的时间序列数据。我尝试将它们对齐到一个数据框中。
下面是一些代码，具有代表性的数据帧，df1、df2 和 df3(我实际上有 n=5，非常感谢适用于所有 n>2 的解决方案):
# 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
我知道为什么会出现此错误，所以我删除了 combined_1_n_2 中的重复索引并重试:
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
为什么会出现此错误？即使这行得通，它也完全是手动的，而且很丑陋。如何对齐 >2 个时间序列并将它们合并到一个数据框中？
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' ] ])
最佳答案
您的特定错误是由于 combined_1_n_2 的列名重复(两列都将命名为“价格”)。您可以重命名列，第二个对齐就可以了。
另一种方法是链接 join 运算符，它会合并索引上的帧，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
关于python - Pandas 将多个数据帧与时间戳索引对齐，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/26366021/