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熊猫放置唯一行以使用groupby和qcut

[英]Pandas drop unique row in order to use groupby and qcut

How do I drop unique? 我如何独特? It is interfering with groupby and qcut. 它干扰了groupby和qcut。

df0 = psql.read_frame(sql_query,conn)
df = df0.sort(['industry','C'], ascending=[False,True] )

Here is my dataframe: 这是我的数据框:

    id                    industry     C
5   28              other industry  0.22
9   32          Specialty Eateries  0.60
10  33                 Restaurants  0.84
1   22  Processed & Packaged Goods  0.07
0   21  Processed & Packaged Goods  0.14
8   31  Processed & Packaged Goods  0.43
11  34  Major Integrated Oil & Gas  0.07
14  37  Major Integrated Oil & Gas  0.50
15  38       Independent Oil & Gas  0.06
18  41       Independent Oil & Gas  0.06
19  42       Independent Oil & Gas  0.13
12  35       Independent Oil & Gas  0.43
16  39       Independent Oil & Gas  0.65
17  40       Independent Oil & Gas  0.91
13  36       Independent Oil & Gas  2.25
2   25    Food - Major Diversified  0.35
3   26     Beverages - Soft Drinks  0.54
4   27     Beverages - Soft Drinks  0.73
6   29         Beverages - Brewers  0.19
7   30         Beverages - Brewers  0.21

And I've used the following code from pandas and qcut to rank column 'C' which sadly went batsh*t on me. 而且我使用了来自pandas和qcut的以下代码来对列“ C”进行排名,可悲的是,列对我而言是batsh * t。

df['rank'] = df.groupby(['industry'])['C'].transform(lambda x: pd.qcut(x,5, labels=range(1,6)))

After researching a bit, the reason qcut threw errors is because of the unique value for industry column, reference to error and another ref to err . 经过研究,qcut抛出错误的原因是因为行业专栏的独特价值, 对错误的引用以及对err的另一引用

Although, I still want to be able to rank without throwing out unique (unique should be assign to the value of 1) if that is possible. 虽然,我仍然希望能够在不放弃唯一性的情况下进行排名(唯一性应分配给值1)。 But after so many tries, I am convinced that qcut can't handle unique and so I am willing to settle for dropping unique to make qcut happy doing its thing. 但是经过如此多的尝试,我坚信qcut无法处理唯一性,因此我愿意接受放弃唯一性以使qcut开心地完成它的事情。

But if there is another way, I'm very curious to know. 但是,如果还有另一种方法,我很想知道。 I really appreciate your help. 非常感谢您的帮助。

Just in case anyone still wants to do this. 以防万一仍然有人想要这样做。 You should be able to do it by selecting only duplicates? 您应该只选择重复项就能做到吗?

df = df[df['industry'].duplicated(keep=False)]

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