使用fit_transform進行資料轉換出現錯誤

# 將 Fare 取 boxcox 後, 看散佈圖, 並計算分數 (執行會有 error, 請試圖修正)
from scipy import stats
df_fixed = copy.deepcopy(df)
"""
Your Code Here, fix the error
"""
from sklearn.preprocessing import MinMaxScaler
scalar = MinMaxScaler( feature_range=(1, 10))
scalar.fit(df_fixed)
df_fixed =  scalar.fit_transform(df_fixed)

df_fixed['Fare'] = stats.boxcox(df_fixed['Fare'])[0]

output=====
ndexError                                Traceback (most recent call last)
<ipython-input-47-da10ee54c24a> in <module>
     10 df_fixed =  scalar.fit_transform(df_fixed)
     11 
---> 12 df_fixed['Fare'] = stats.boxcox(df_fixed['Fare'])[0]
     13 sns.distplot(df_fixed['Fare'][:train_num])
     14 plt.show()

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

# 將 Fare 取 boxcox 後, 看散佈圖, 並計算分數 (執行會有 error, 請試圖修正)
from scipy import stats
df_fixed = copy.deepcopy(df)
"""
Your Code Here, fix the error
"""
from sklearn.preprocessing import MinMaxScaler
scalar = MinMaxScaler( feature_range=(1, 10))
scalar.fit(df_fixed)
df_fixed =  scalar.fit_transform(df_fixed)

df_fixed['Fare'] = stats.boxcox(df_fixed['Fare'])[0]

output=====
ndexError                                Traceback (most recent call last)
<ipython-input-47-da10ee54c24a> in <module>
     10 df_fixed =  scalar.fit_transform(df_fixed)
     11 
---> 12 df_fixed['Fare'] = stats.boxcox(df_fixed['Fare'])[0]
     13 sns.distplot(df_fixed['Fare'][:train_num])
     14 plt.show()

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices