[Python] why result of scaling each column always equal to zero?

I am using minmaxscaler trying to scaling each column. The scaled result for each column is always all zero. For example , below the values of df_test_1 after finishing scaling is all zero. But even with all values of zero, using inverse_transferm from this values of zero can still revert back to original values. But why the results of scaled are shown all zero?

from sklearn.preprocessing import MinMaxScaler
df_dict={'A':[-1,-0.5,0,1],'B':[2,6,10,18]}
df_test=pd.DataFrame(df_dict)
print('original scale data')
print(df_test)
scaler_model_list=[]
df_test_1=df_test.copy()
for col in df_test.columns:
scaler = MinMaxScaler()
scaler_model_list.append(scaler) # need to save scalerfor each column since there are different if we want to use inverse_transform() later
df_test_1.loc[:,col]=scaler.fit_transform(df_test_1.loc[:,col].values.reshape(1,-1))[0]

print('after finishing scaling')
print(df_test_1)
print('after inverse transformation')
print(scaler_model_list[0].inverse_transform(df_test_1.iloc[:,0].values.reshape(1,-1)))
print(scaler_model_list[1].inverse_transform(df_test_1.iloc[:,1].values.reshape(1,-1)))


original scale data
A B
0 -1.0 2
1 -0.5 6
2 0.0 10
3 1.0 18
after finishing scaling
A B
0 0.0 0
1 0.0 0
2 0.0 0
3 0.0 0
after inverse transformation
[[-1. -0.5 0. 1. ]]
[[ 2. 6. 10. 18.]]

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