pandas groupby并在以每组1开头的组中排名 [英] pandas groupby and rank within groups that start with 1 for each group

问题描述

我有一个数据框:

将熊猫作为pd导入

df = pd.DataFrame([[1, 'a'],
                    [1, 'a'],
                    [1, 'b'],
                    [1, 'a'],
                    [2, 'a'],
                    [2, 'b'],
                    [2, 'a'],
                    [2, 'b'],
                    [3, 'b'],
                    [3, 'a'],
                    [3, 'b'],

                   ], columns=['session', 'issue'])
df

我想在会议中对问题进行排名. 我尝试过:

I would like to rank issues within sessions. I tried with:

df.groupby(['session', 'issue']).size().rank(ascending=False, method='dense')

session  issue
1        a        1.0
         b        3.0
2        a        2.0
         b        2.0
3        a        3.0
         b        2.0
dtype: float64

我需要的是这样的结果:

What I need is result like this one:

1. 对于小组会议= 1，有3个问题和1个b问题，因此 对于第1组，排名为a = 1和b = 2
2. 对于分组会话= 2，两个等级均相等，因此它们的等级应相同= 1
3. 对于小组会议= 3，有b个问题，一个a问题，因此等级应为b = 1和a = 2

1. for group session=1, there are three a issues and one b issue, so for group 1, ranks are a = 1 and b = 2
2. for group session=2, both ranks are equal so their rank should be the same = 1
3. for group session=3, there are to b issues and one a issue, so ranks should be b=1 and a=2

无论如何，为什么每个组的排名都不是从1、2、3 ...开始?

Anyway, why for each group ranks don't start from 1, 2, 3...?

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