按索引合并两个数据帧 [英] Merge two dataframes by index

问题描述

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
   

如何合并索引以获取:

  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:

I ask because it is my understanding that merge() i.e. df1.merge(df2) uses columns to do the matching. In fact, doing this I get:

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

在索引上合并是不好的做法吗?不可能吗?如果是这样，我如何将索引移到名为索引"的新列中?

Is it bad practice to merge on index? Is it impossible? If so, how can I shift the index into a new column called "index"?

推荐答案

使用 merge，默认是内连接:

Use merge, which is an inner join by default:

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

或加入，默认是左连接:

Or join, which is a left join by default:

df1.join(df2)

或者 concat)，默认是外连接:

Or concat), which is an outer join by default:

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

示例:

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

这篇关于按索引合并两个数据帧的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！