pandas join()可以工作，但是concat()失败 [英] Pandas join() works, but concat() fails

问题描述

我想使用Pandas 0.14.1对具有相同行索引的两个数据框进行外部联接.

I want to perform outer join of two dataframes with the same row index using Pandas 0.14.1.

df1的形状为456,1，df2的形状为139,5.

The shape of df1 is 456,1 and df2 is 139,5.

df2中的大多数键都在df1中找到:

Most of the keys in df2 are found in df1:

[in] print len(list(set(df2.index)-set(df1.index))) 
[out] 16

join起作用:

[in] df3=df1.join(df2,how='outer')
[in] df3.shape
[out] 473,6

concat失败:

[in] df3=pd.concat([df1,df2],axis=1,join='outer')
[out] ValueError: Shape of passed values is (6, 473), indices imply (6, 472)

这可能是什么原因造成的?

What may cause this?

推荐答案

如果其中一个索引具有重复值，则可能会出现此错误.例如，

You could get this error if one of the indexes has duplicate values. For instance,

import pandas as pd
df1 = pd.DataFrame(np.random.random((5,1)), index=list('AACDE'), 
                   columns=['foo'])
df2 = pd.DataFrame(np.random.random((4,1)), index=list('CDEF'), 
                   columns=['bar'])

然后

In [50]: df1.join(df2, how='outer')
Out[50]: 
        foo       bar
A  0.846814       NaN
A  0.638571       NaN
C  0.516051  0.573165
D  0.789398  0.095466
E  0.921592  0.970619
F       NaN  0.061434

但是

In [51]: pd.concat([df1,df2], axis=1, join='outer')
ValueError: Shape of passed values is (2, 6), indices imply (2, 5)

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