Pandas 将多个数据帧与时间戳索引对齐

Posted 2023-03-12

【中文标题】Pandas 将多个数据帧与时间戳索引对齐

【英文标题】：Pandas aligning multiple dataframes with TimeStamp index

【发布时间】：2014-12-09 13:18:35

【问题描述】：

在过去的几天里，这一直是我生活的祸根。我有许多 Pandas 数据框，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单个数据框中。

下面是一些代码，带有代表性的数据框 df1、df2 和 df3（我实际上有 n=5，并且希望有一个适用于所有 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，因此我删除了 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？即使这有效，它也是完全手动且丑陋的。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' ] ])   

【参考方案1】：

您的具体错误是由于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，然后第二个对齐就可以了。

另一种方法是链接join 运算符，它合并索引上的帧，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

【讨论】：

谢谢，这太令人惊讶了；所以如果我进行链连接，align() 是不必要的？

正确，join 为您处理索引逻辑。

谢谢，出于好奇：您能否简要说明一下何时可能需要使用 align()？因为在我看来，join() 似乎一次性处理了对齐和连接。

我能想到的实际案例不多，但如果由于某种原因只需要对齐索引，使用align会节省一点时间。