无法在循环中更新/替换组值（Python，“ AttributeError：无法设置属性”）

Question

无法用新值替换group的值（在循环内）

我看到了有些带有相同错误代码的类似问题，他们提到“属性”和“设置者”的必要性，但我认为这不适用/最简单的方法解决我的特殊问题。

• 我相信可以使用.merge，.transform，lambda甚至是简单的=赋值来简化解决方案...
• 此外，我的问题出现在一个循环和groupby组中，这使其与具有类似错误消息的其他问题更加不同

获取“ AttributeError: can't set attribute”

DataFrame：

sample_data = [['USA', 'gdp', 2001, 10],['USA', 'avgIQ', 2001, 100],['USA', 'people', 2001, 1000],['USA', 'dragons', 2001, 3],['CHN', 'gdp', 2001, 12], ['CHN', 'avgIQ', 2001, 120],['CHN', 'people', 2001, 2000],['CHN', 'dragons', 2001, 1],['RUS', 'gdp', 2001, 11],['RUS', 'avgIQ', 2001, 105], ['RUS', 'people', 2001, 1500],['RUS', 'dragons', 2001, np.nan],['USA', 'gdp', 2002, 12],['USA', 'avgIQ', 2002, 105],['USA', 'people', 2002, 1200], ['USA', 'dragons', 2002, np.nan],['CHN', 'gdp', 2002, 14],['CHN', 'avgIQ', 2002, 127],['CHN', 'people', 2002, 3100],['CHN', 'dragons', 2002, 4], ['RUS', 'gdp', 2002, 11],['RUS', 'avgIQ', 2002, 99],['RUS', 'people', 2002, 1600],['RUS', 'dragons', 2002, np.nan],['USA', 'gdp', 2003, 15], ['USA', 'avgIQ', 2003, 115],['USA', 'people', 2003, 2000],['USA', 'dragons', 2003, np.nan],['CHN', 'gdp', 2003, 16],['CHN', 'avgIQ', 2003, 132], ['CHN', 'people', 2003, 4000],['CHN', 'dragons', 2003, 6],['RUS', 'gdp', 2003, 11],['RUS', 'avgIQ', 2003, 108],['RUS', 'people', 2003, 2000], ['RUS', 'dragons', 2003, np.nan],['USA', 'gdp', 2004, 18],['USA', 'avgIQ', 2004, 111],['USA', 'people', 2004, 2500],['USA', 'dragons', 2004, np.nan], ['CHN', 'gdp', 2004, 18],['CHN', 'avgIQ', 2004, 140],['CHN', 'people', 2004, np.nan],['CHN', 'dragons', 2004, np.nan], ['RUS', 'gdp', 2004, 15],['RUS', 'avgIQ', 2004, 103],['RUS', 'people', 2004, 2800],['RUS', 'dragons', 2004, np.nan], ['USA', 'gdp', 2005, 23],['USA', 'avgIQ', 2005, 111],['USA', 'people', 2005, 3700],['USA', 'dragons', 2005, 8],['CHN', 'gdp', 2005, 22], ['CHN', 'avgIQ', 2005, 143],['CHN', 'people', 2005, 6000],['CHN', 'dragons', 2005, 15],['RUS', 'gdp', 2005, 17],['RUS', 'avgIQ', 2005, np.nan], ['RUS', 'people', 2005, 3000],['RUS', 'dragons', 2005, 3]]

sample_df = pd.DataFrame(sample_data, columns = ['A','B','C','D'])

sample_df['C'] = sample_df['C'].astype(float) 
sample_df.head()

Data columns (total 4 columns):
A    60 non-null object
B    60 non-null object
C    60 non-null float64
D    50 non-null float64
dtypes: float64(2), object(2)

from impyute.imputation.cs import mice

  

循环中的以下行是问题所在：

     

group['D'].values = ((mice(group.apply({'C': lambda x: x.values, 'D': lambda y: y.values})))[1]).values

     

请注意我在输出中放置的主题标签。

for group_index, group in sample_group:

    if group.isnull().values.any() == True:

        print(group)
        print(group['D'].values)
        print(mice(group.apply({'C': lambda x: x.values, 'D': lambda y: y.values})))
        print((mice(group.apply({'C': lambda x: x.values, 'D': lambda y: y.values})))[1])
        print(((mice(group.apply({'C': lambda x: x.values, 'D': lambda y: y.values})))[1]).values)


        group['D'].values = ((mice(group.apply({'C': lambda x: x.values, 'D': lambda y: y.values})))[1]).values


        print(group)


    else:
        print('Checked group but could not satisfy condition', group_index)

Checked group but could not satisfy condition ('CHN', 'avgIQ')  #Does not have any nan values
      A        B        C     D
7   CHN  dragons 2,001.00  1.00
19  CHN  dragons 2,002.00  4.00
31  CHN  dragons 2,003.00  6.00
43  CHN  dragons 2,004.00   nan   #Prints group because it has nan
55  CHN  dragons 2,005.00 15.00
[ 1.  4.  6. nan 15.]             #Prints values of 'D'
         0     1
0 2,001.00  1.00
1 2,002.00  4.00
2 2,003.00  6.00
3 2,004.00 10.86                  #Imputes the nan value and prints
4 2,005.00 15.00
0    1.00
1    4.00
2    6.00
3   10.86                         #Prints only the column with the new imputed value
4   15.00
Name: 1, dtype: float64
[ 1.          4.          6.         10.85714286 15.        ] #Prints the new values for the column





**AttributeError: can't set attribute**

所需的输出（在当前有效的输出之后^^^）：

      A        B        C     D
7   CHN  dragons 2,001.00  1.00
19  CHN  dragons 2,002.00  4.00
31  CHN  dragons 2,003.00  6.00
43  CHN  dragons 2,004.00 10.86   #Replace the original 'D' column for that group, with the new value(s)
55  CHN  dragons 2,005.00 15.00

最终，我将要创建一个新的df，其中包含所有没有nan的原始组，以及已推算nan的更新组。

Answer 1

尝试一下：

group['D'] = ((mice(group.apply({'C': lambda x: x.values, 'D': lambda y: y.values})))[1]).values