在删除 pandas 数据框中的重复项后替换特定的列值 [英] Replacing specific column values after removing duplicates in a pandas dataframe

问题描述

我是pandas的初学者(如果使用了错误的术语，我深表歉意)，目前正在从事基因组计划.使用drop_duplicates()后，在处理数据框列时遇到麻烦.我想更改删除重复项后保留的ID的突变"列中的列值，以表明此ID具有多个突变.

I'm a beginner at pandas (I apologize if i'm using the wrong terminology) and i am currently working on a genomics project. I'm having trouble manipulating dataframes columns after using drop_duplicates(). I want to change the column values in the column 'mutation' of the id that is kept after dropping duplicates to indicate that this id has multiple mutations.

df = pd.DataFrame([
('MYC', 'nonsense', 's1'),
('MYC', 'missense', 's1'),
('MYCL', 'nonsense', 's1'),
('MYCL', 'missense', 's2'),
('MYCN', 'missense', 's3'),
('MYCN', 'UTR', 's1'),
('MYCN', 'nonsense', 's1')
], columns=['id', 'mutation', 'sample'])

print(df)

结果:

     id  mutation sample
0   MYC  nonsense     s1
1   MYC  nonsense     s1
2   MYC  missense     s1
3  MYCL  nonsense     s1
4  MYCL  missense     s2
5  MYCN  missense     s3
6  MYCN       UTR     s1
7  MYCN  nonsense     s1

我尝试使用drop_duplicates()，但我越来越接近想要的东西了.但是，如何将突变"列中的值更改为多"呢?

I tried using drop_duplicates() and i am getting close to what i want. But how do i change the value in the column 'mutation' to 'multi'?

 print(df.drop_duplicates(subset=('sample','id')))
     id  mutation sample
0   MYC  nonsense     s1
3  MYCL  nonsense     s1
4  MYCL  missense     s2
5  MYCN  missense     s3
6  MYCN       UTR     s1

我想要什么:

     id  mutation sample
0   MYC  multi        s1
3  MYCL  nonsense     s1
4  MYCL  missense     s2
5  MYCN  missense     s3
6  MYCN  multi        s1

推荐答案

duplicated

mask = df.duplicated(['id', 'sample'], keep=False)
df.assign(mutation=df.mutation.mask(mask, 'multi')).drop_duplicates()

     id  mutation sample
0   MYC     multi     s1
2  MYCL   nonsens     s1
3  MYCL  missense     s2
4  MYCN  missense     s3
5  MYCN     multi     s1

---

groupby

---

groupby

df.groupby(['id', 'sample'], sort=False).mutation.pipe(
    lambda g: g.first().mask(g.size() > 1, 'multi')
).reset_index().reindex(df.columns, axis=1)

     id  mutation sample
0   MYC     multi     s1
1  MYCL   nonsens     s1
2  MYCL  missense     s2
3  MYCN  missense     s3
4  MYCN     multi     s1

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