pandas 更改重复行的特定列值 [英] pandas change a specific column value of duplicate rows

问题描述

在此处使用示例将所有重复的行拖放到Python Pandas中

让我们说我不想删除重复项，而是更改子集中列之一中的数据值.

因此，根据示例，如果我们使用subset = ['A'，'C']来标识重复项，那么我想将第1行的列'A'从foo更改为foo1.

我执行此操作的方法很复杂，但必须有一种更简单的方法来利用矢量化/内置功能.<​​/p>

原始df:

    A   B   C
0   foo 0   A
1   foo 1   A
2   foo 1   B
3   bar 1   A

所需的df:

    A   B   C
0   foo 0   A
1   foo1 1   A
2   foo 1   B
3   bar 1   A

解决方案

您可以使用cumcount并执行类似的操作

>>> c = df.groupby(["A","C"]).cumcount()
>>> c = c.replace(0, '').astype(str)
>>> df["A"] += c
>>> df
      A  B  C
0   foo  0  A
1  foo1  1  A
2   foo  1  B
3   bar  1  A

之所以可行，是因为cumcount给了我们

>>> df.groupby(["A","C"]).cumcount()
0    0
1    1
2    0
3    0
dtype: int64

Using the example here Drop all duplicate rows in Python Pandas

Lets say I don't want to drop the duplicates but change the value of the data in one of the columns in the subset.

So as per the example, if we use subset=['A','C'] to identify duplicates then I want to change row 1 column 'A' from foo to foo1.

I have a complicated way of doing this but there must be a more simple way that takes advantage of vectorization/built-in features.

Original df:

    A   B   C
0   foo 0   A
1   foo 1   A
2   foo 1   B
3   bar 1   A

Desired df:

    A   B   C
0   foo 0   A
1   foo1 1   A
2   foo 1   B
3   bar 1   A

解决方案

You could use cumcount and do something like

>>> c = df.groupby(["A","C"]).cumcount()
>>> c = c.replace(0, '').astype(str)
>>> df["A"] += c
>>> df
      A  B  C
0   foo  0  A
1  foo1  1  A
2   foo  1  B
3   bar  1  A

This works because the cumcount gives us

>>> df.groupby(["A","C"]).cumcount()
0    0
1    1
2    0
3    0
dtype: int64

这篇关于 pandas 更改重复行的特定列值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！