嗨,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,因为据我了解,merge()
即df1.merge(df2)
使用列进行匹配。实际上,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
在索引上合并是不好的做法吗?不可能吗 如果是这样,如何将索引移到称为“索引”的新列中?
谢谢
使用merge
,默认情况下是内部联接:
pd.merge(df1, df2, left_index=True, right_index=True)
或join
,默认情况下为左连接:
df1.join(df2)
或concat
,默认情况下为外部联接:
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
本文收集自互联网,转载请注明来源。
如有侵权,请联系 [email protected] 删除。
我来说两句