我有一个有3列的Dataframe，两个是类别数据，一个是float16。当我执行groupby并在agg中运行lambda特定函数以根据dtype对每一列进行不同处理时，分类列上会有一个下降。

如果这样做的话，它就会奏效。

i=pd.DataFrame({"A":["a","a","a","b","c","c"],"B":[1,2,3,4,5,6],"C":[ "NaN" ,"b","NaN","b","c","c"]})
i['A'] = i['A'].astype('category')   
i['B'] = i['B'].astype('float16')   
i.groupby("A", as_index=False)[["B","C"]].agg(lambda x: x.mean() if np.dtype(x)=='float16' else x.value_counts().index[0])

输出，这是我想要得到的是：

    A   B   C
0   a   2.0 NaN
1   b   4.0 b
2   c   5.5 c

但是，每当我声明C列为绝对列时，python就会自动删除列C。

i=pd.DataFrame({"A":["a","a","a","b","c","c"],"B":[1,2,3,4,5,6],"C":[ "NaN" ,"b","NaN","b","c","c"]})
i['A'] = i['A'].astype('category')   
i['B'] = i['B'].astype('float16')   
i['C'] = i['C'].astype('category')  
i.groupby("A", as_index=False)[["B","C"]].agg(lambda x: x.mean() if np.dtype(x)=='float16' else x.value_counts().index[0])

答案如下：

['C'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.
A   B
0   a   2.0
1   b   4.0
2   c   5.5

是否有人知道groupby中的agg不能处理分类列？