查找熊猫数据框中的所有重复行
我希望能够在不事先知道名称和列数的情况下获取数据集中重复行的所有实例的索引.所以假设我有这个:
I would like to be able to get the indices of all the instances of a duplicated row in a dataset without knowing the name and number of columns beforehand. So assume I have this:
col
1 | 1
2 | 2
3 | 1
4 | 1
5 | 2
我希望能够获得[1, 3, 4]和[2, 5].有什么办法可以做到这一点?听起来确实很简单，但是由于我事先不知道各列，所以无法执行df[col == x...]之类的事情.
I'd like to be able to get [1, 3, 4] and [2, 5]. Is there any way to achieve this? It sounds really simple, but since I don't know the columns beforehand I can't do something like df[col == x...].
首先过滤所有 groupby 与apply或转换index
First filter all duplicated rows and then groupby with apply or convert index to_series:
df = df[df.col.duplicated(keep=False)]
a = df.groupby('col').apply(lambda x: list(x.index))
print (a)
col
1 [1, 3, 4]
2 [2, 5]
dtype: object
a = df.index.to_series().groupby(df.col).apply(list)
print (a)
col
1 [1, 3, 4]
2 [2, 5]
dtype: object
如果需要嵌套列表:
L = df.groupby('col').apply(lambda x: list(x.index)).tolist()
print (L)
[[1, 3, 4], [2, 5]]
If need use only first column is possible selected by position with iloc:
a = df[df.iloc[:,0].duplicated(keep=False)]
.groupby(df.iloc[:,0]).apply(lambda x: list(x.index))
print (a)
col
1 [1, 3, 4]
2 [2, 5]
dtype: object