合并两个数据框按索引排序

Question

嗨，我有以下数据帧：

> df1
  id begin conditional confidence discoveryTechnique  
0 278    56       false        0.0                  1   
1 421    18       false        0.0                  1 

> df2
   concept 
0  A  
1  B

如何合并索引以获取：

  id begin conditional confidence discoveryTechnique   concept 
0 278    56       false        0.0                  1  A 
1 421    18       false        0.0                  1  B

我这样问是因为我的理解是merge()即df1.merge(df2)使用列来进行匹配。事实上，这样做我得到：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 4618, in merge
    copy=copy, indicator=indicator)
  File "/usr/local/lib/python2.7/dist-packages/pandas/tools/merge.py", line 58, in merge
    copy=copy, indicator=indicator)
  File "/usr/local/lib/python2.7/dist-packages/pandas/tools/merge.py", line 491, in __init__
    self._validate_specification()
  File "/usr/local/lib/python2.7/dist-packages/pandas/tools/merge.py", line 812, in _validate_specification
    raise MergeError('No common columns to perform merge on')
pandas.tools.merge.MergeError: No common columns to perform merge on

按索引合并是不是不好？不可能吗？如果是，如何将索引移到名为“index”的新列中？

谢谢

Answer 1

使用^{}，这是默认的内部连接：

pd.merge(df1, df2, left_index=True, right_index=True)

或者^{}，默认情况下是左连接：

df1.join(df2)

或者^{}，默认情况下是外部连接：

pd.concat([df1, df2], axis=1)

样本：

df1 = pd.DataFrame({'a':range(6),
                    'b':[5,3,6,9,2,4]}, index=list('abcdef'))

print (df1)
   a  b
a  0  5
b  1  3
c  2  6
d  3  9
e  4  2
f  5  4

df2 = pd.DataFrame({'c':range(4),
                    'd':[10,20,30, 40]}, index=list('abhi'))

print (df2)
   c   d
a  0  10
b  1  20
h  2  30
i  3  40

---

#default inner join
df3 = pd.merge(df1, df2, left_index=True, right_index=True)
print (df3)
   a  b  c   d
a  0  5  0  10
b  1  3  1  20

#default left join
df4 = df1.join(df2)
print (df4)
   a  b    c     d
a  0  5  0.0  10.0
b  1  3  1.0  20.0
c  2  6  NaN   NaN
d  3  9  NaN   NaN
e  4  2  NaN   NaN
f  5  4  NaN   NaN

#default outer join
df5 = pd.concat([df1, df2], axis=1)
print (df5)
     a    b    c     d
a  0.0  5.0  0.0  10.0
b  1.0  3.0  1.0  20.0
c  2.0  6.0  NaN   NaN
d  3.0  9.0  NaN   NaN
e  4.0  2.0  NaN   NaN
f  5.0  4.0  NaN   NaN
h  NaN  NaN  2.0  30.0
i  NaN  NaN  3.0  40.0

Answer 2

一个愚蠢的错误：连接失败，因为索引数据类型不同。这并不明显，因为两个表都是同一原始表的透视表。重置索引后，jupyter中的索引看起来相同。它只是在保存到excel时才曝光。。。

用df1[['key']] = df1[['key']].apply(pd.to_numeric)修复

希望这能节省一个小时！

Answer 3

可以使用concat([df1, df2, ...], axis=1)将两个或多个按索引对齐的df连接起来：

pd.concat([df1, df2, df3, ...], axis=1)

或者merge用于通过自定义字段/索引连接：

# join by _common_ columns: `col1`, `col3`
pd.merge(df1, df2, on=['col1','col3'])

# join by: `df1.col1 == df2.index`
pd.merge(df1, df2, left_on='col1' right_index=True)

或join通过索引连接：

 df1.join(df2)