Pandas：用唯一值替换多个列值(Pandas: Replace multiple column values by unique value)

 我有一个有许多“对象”列的熊猫数据框，其中每个列都包含许多值（模态）。 然后，我想只保留每列10个最常见的模式 ，其他人则用'Oth'替换。 
 
 例如，如果我有一个包含4个不同值的列'obj_col1'： 
 
obj_col1
'A'
'A'
'B'
'C'
'B'
'D'
 
 我想保留2个最常用的，这里是'A'和'B'，并用'Oth'代替其余部分： 
 
obj_col2
'A'
'A'
'B'
'Oth'
'B'
'Oth'
 
 一个对象列（分类变量）的代码是： 
 
# sorted list of modalities of 'categ_var' 
list_freq_modal = df['categ_var'].value_counts().index.tolist()
# replace all the modalities except the first 10 by 'Oth'
df['categ_var'].replace(list_freq_modal[10:],'Oth', inplace=True)
 
 但是我有一个错误：'NoneType'对象没有任何属性'any' 
 
 你有没有想法以更优化的方式实现它？ 

I have a pandas DataFrame with many "object" columns where each of them contains many values (modalities). Then, I want to keep only the 10 most frequent modalities for each column and the others replace by 'Oth'.
 
For example, if I have a column 'obj_col1' which contains 4 different values: 
 
obj_col1
'A'
'A'
'B'
'C'
'B'
'D'
 
and I want to keep 2 the most frequent, here 'A' and 'B', and replace the rest by 'Oth':
 
obj_col2
'A'
'A'
'B'
'Oth'
'B'
'Oth'
 
A piece of code for one object column (categorical variable) is:
 
# sorted list of modalities of 'categ_var' 
list_freq_modal = df['categ_var'].value_counts().index.tolist()
# replace all the modalities except the first 10 by 'Oth'
df['categ_var'].replace(list_freq_modal[10:],'Oth', inplace=True)
 
But I have an error : 'NoneType' object has no attribute 'any'
 
Have you any idea have implement it in more optimal way ?

原文：https://stackoverflow.com/questions/47287191