Python 按索引合并两个数据帧
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Merge two dataframes by index
提问by brucezepplin
Hi I have the following dataframes:
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
How do I merge on the indices to get:
我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
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:
我问是因为我的理解是merge()iedf1.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
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"?
在索引上合并是不好的做法吗？不可能吗？如果是这样，我如何将索引移动到一个名为“索引”的新列中？
Thanks
谢谢
回答by jezrael
Use merge, which is inner join by default:
使用merge，默认为内连接：
pd.merge(df1, df2, left_index=True, right_index=True)
Or join, which is left join by default:
或者join，默认情况下是左连接：
df1.join(df2)
Or concat, which is outer join by default:
或者concat，默认情况下是外连接：
pd.concat([df1, df2], axis=1)
Samples:
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
回答by MaxU
you can use concat([df1, df2, ...], axis=1)in order to concatenate two or more DFs aligned by indexes:
您可以使用concat([df1, df2, ...], axis=1)来连接两个或多个按索引对齐的 DF：
pd.concat([df1, df2, df3, ...], axis=1)
or mergefor concatenating by custom fields / indexes:
或合并以按自定义字段/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)
or joinfor joining by index:
或join按索引加入：
df1.join(df2)
回答by vicpal
By default:joinis a column-wise left joinpd.mergeis a column-wise inner joinpd.concatis a row-wise outer join
默认情况下：join是按列左连接pd.merge是按列内连接pd.concat是按行外连接
pd.concat:
takes Iterable arguments. Thus, it cannot take DataFrames directly (use [df,df2])
Dimensions of DataFrame should match along axis
pd.concat:
接受可迭代的参数。因此，它不能直接使用 DataFrames（使用[df,df2]）
DataFrame 的维度应该沿轴匹配
Joinand pd.merge:
can take DataFrame arguments
Join和pd.merge:
可以采用 DataFrame 参数
回答by Stephen Morrell
A silly bug that got me: the joins failed because index dtypesdiffered. This was not obvious as both tables were pivot tables of the same original table. After reset_index, the indices looked identical in Jupyter. It only came to light when saving to Excel...
一个让我感到愚蠢的错误：连接失败，因为索引dtypes不同。这并不明显，因为两个表都是同一个原始表的数据透视表。之后reset_index，索引在 Jupyter 中看起来相同。只有在保存到 Excel 时才出现...
Fixed with: df1[['key']] = df1[['key']].apply(pd.to_numeric)
固定： df1[['key']] = df1[['key']].apply(pd.to_numeric)
Hopefully this saves somebody an hour!
希望这可以为某人节省一个小时！
回答by vignesh babu
If u want to join two dataframes in pandas you can simply use available attributes like mergeor concatenate.
For example if I have two dataframes df1and df2I can join them by:
如果你想在 Pandas 中加入两个数据帧，你可以简单地使用可用的属性，如merge或concatenate。例如，如果我有两个数据框df1，df2我可以通过以下方式加入它们：
newdataframe=merge(df1,df2,left_index=True,right_index=True)
