问题描述
df.dtypes
Close float64
eqId int64
date object
IntDate int64
expiry int64
delta int64
ivMid float64
conf float64
Skew float64
psc float64
vol_B category
dtype: object
gb = df.groupby([df['vol_B'],df['expiry']])
gb.describe()
我收到一条很长的错误消息,最后一行是
I get a long error message with the final line being
AttributeError: 'Categorical' object has no attribute 'flags'
当我分别对它们分别执行groupby
时,它们各自(独立地)工作都很好,我只是不能执行多个groupby
,而其中一个变量是"bin".
When I perform a groupby
on each of them separately they each (independently) work great, I just can not perform multiple groupby
with one of the variables being a "bin."
此外,当我使用其他2个变量时,我可以执行多个groupby
& ndash,我可以成功执行以下操作:
Also, when I use 2 other variables I am able to perform multiple groupby
&ndash I successfully performed this:
gb = df.groupby([df['delta'],df['expiry']])
推荐答案
我遇到了与OP类似的问题,并且在寻找解决方案时发现了这个问题.在浏览了分类变量的文档之后,一个对我有用的简单技巧是更改了分组前分类变量的类型.
I was facing a similar issue as the OP and found this question while looking for solutions. A simple hack that worked for me after going through the pandas documentation for categorical variables was to change the type of the categorical variable before grouping.
由于vol_B是您的情况下的分类变量,因此您应该尝试以下操作
Since vol_B is the categorical variable in your case, you should try the following
#Depending on the content of vol_B you can do astype(int) or astype(float) as well.
gb = df.groupby([df['vol_B'].astype(str), df['expiry']])
我还没有详细说明为什么这种方法有效,而那没有用,但是如果我进入其中,我将更新答案.
I haven't gone into the details of why this works and that doesn't but if I get into it, I will update the answer.
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