如果值相同,Python Pandas将列从df复制到另一个 [英] Python Pandas copying column from df to another if values same
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问题描述
我有两个数据框:
DF ONE:
ID A B C
1 x y z
1 x y z
2 x y z
2 x y z
2 x y z
3 x y z
DF 2:
ID D E F
1 a b c1
2 a b c2
3 a b c3
我想以DF TWO为例的列E
,如果ID相同,则将其放在DF ONE上,因此我将得到以下输出:
I want to take column E
for example from DF TWO, and put it on DF ONE, if the ID is the same, so after I will get this output:
ID A B C F
1 x y z c1
1 x y z c1
2 x y z c2
2 x y z c2
2 x y z c2
3 x y z c3
感谢您的帮助
推荐答案
Another solution is map
by Series
:
s = df2.set_index('ID')['F']
print (s)
ID
1 c1
2 c2
3 c3
Name: F, dtype: object
df1['F'] = df1['ID'].map(s)
print (df1)
ID A B C F
0 1 x y z c1
1 1 x y z c1
2 2 x y z c2
3 2 x y z c2
4 2 x y z c2
5 3 x y z c3
时间:
#[60000 rows x 5 columns]
df1 = pd.concat([df1]*10000).reset_index(drop=True)
In [115]: %timeit pd.merge(df1, df2[['ID', 'F']],how='left')
100 loops, best of 3: 11.1 ms per loop
In [116]: %timeit df1['ID'].map(df2.set_index('ID')['F'])
100 loops, best of 3: 3.18 ms per loop
In [117]: %timeit df1['ID'].map(df2.set_index('ID')['F'].to_dict())
100 loops, best of 3: 3.36 ms per loop
In [118]: %timeit df1['ID'].map({k:v for k, v in df2[['ID', 'F']].as_matrix()})
100 loops, best of 3: 3.44 ms per loop
In [119]: %%timeit
...: df2.index = df2['ID']
...: df1['F1'] = df1['ID'].map(df2['F'])
...:
100 loops, best of 3: 3.33 ms per loop
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