按索引合并两个数据框

Question

我有以下数据框：

 > 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

如何合并索引以获得：

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

我问是因为我的理解是 merge() 即 df1.merge(df2) 使用列进行匹配。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

合并索引是不好的做法吗？这是不可能的吗？如果是这样，我怎样才能将索引转移到一个名为“索引”的新列中？

原文由 brucezepplin 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

使用 merge ，默认为内连接：

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

或者 join ，默认是左连接：

 df1.join(df2)

或者 concat )，默认是外连接：

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

样品：

 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

原文由 jezrael 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 2

您可以使用 concat([df1, df2, …], axis=1) 连接两个或多个按索引对齐的 DF：

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

或者 合并 以通过自定义字段/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)

或按索引 加入：

  df1.join(df2)

原文由 MaxU - stop genocide of UA 发布，翻译遵循 CC BY-SA 4.0 许可协议