获取 groupby 和 nlargest 之后的行索引
我有一个大型数据框，我想使用 groupby 和 nlargest 来查找每个组的第二大、第三、第四和第五大值。我有超过 500 个组，每个组有超过 1000 个值。我在数据框中还有其他列，我想在应用groupby和nlargest后保留它们。我的数据框看起来像这样
df = pd.DataFrame({
'group': [1,2,3,3,4, 5,6,7,7,8],
'a': [4, 5, 3, 1, 2, 20, 10, 40, 50, 30],
'b': [20, 10, 40, 50, 30, 4, 5, 3, 1, 2],
'c': [25, 20, 5, 15, 10, 25, 20, 5, 15, 10]
})
要查找列 a 的每组的第二、第三、第四大等，我使用
secondlargest = df.groupby(['group'], as_index=False)['a'].apply(lambda grp: grp.nlargest(2).min())
它返回
group a
0 1 4
1 2 5
2 3 1
3 4 2
4 5 20
5 6 10
6 7 40
7 8 30
我需要列 b 和 c 出现在此结果数据框中。我使用以下内容对原始数据帧进行子集化，但它返回一个空数据帧。我应该如何修改代码？
secondsubset = df[df.groupby(['group'])['a'].apply(lambda grp: grp.nlargest(2).min())]
I have a large dataframe where I want to use groupby and nlargest to look for the second largest, third, fourth and fifth largest value of each group. I have over 500 groups and each group has over 1000 values. I also have other columns in the dataframe which I want to keep after applying groupby and nlargest. My dataframe looks like this
df = pd.DataFrame({
'group': [1,2,3,3,4, 5,6,7,7,8],
'a': [4, 5, 3, 1, 2, 20, 10, 40, 50, 30],
'b': [20, 10, 40, 50, 30, 4, 5, 3, 1, 2],
'c': [25, 20, 5, 15, 10, 25, 20, 5, 15, 10]
})
To look for second, third, fourth largest and so on of each group for column a I use
secondlargest = df.groupby(['group'], as_index=False)['a'].apply(lambda grp: grp.nlargest(2).min())
which returns
group a
0 1 4
1 2 5
2 3 1
3 4 2
4 5 20
5 6 10
6 7 40
7 8 30
I need columns b and c present in this resulting dataframe. I use the following to subset the original dataframe but it returns an empty dataframe. How should I modify the code?
secondsubset = df[df.groupby(['group'])['a'].apply(lambda grp: grp.nlargest(2).min())]
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评论（2）
如果我正确理解你的目标，你应该能够删除
as_index=False，使用idxmin而不是min，将结果传递给df.loc：If I understand your goal correctly, you should be able to just drop
as_index=False, useidxmininstead ofmin, pass the result todf.loc:您可以使用 agg lambda。更整洁了
You can uses agg lambda. It is neater