lambda function to scale column in pandas dataframe returns: “‘float’ object has no attribute ‘min'”

I am just getting started in Python and Machine Learning and have encountered an issue which I haven’t been able to fix myself or with any other online resource. I am trying to scale a column in a pandas dataframe using a lambda function in the following way:

X['col1'] = X['col1'].apply(lambda x: (x - x.min()) / (x.max() - x.min()))

and get the following error message:

‘float’ object has no attribute ‘min’

I have tried to convert the data type into integer and the following error is returned:

‘int’ object has no attribute ‘min’

I believe I am getting something pretty basic wrong, hope anyone can point me in the right direction.

Answer

I think apply here is not necessary, because exist faster vectorized solution – change x to column X['col1']:

X = pd.DataFrame({'col1': [100,10,1,20,10,-20,200]})
X['col2'] = (X['col1'] - X['col1'].min()) / (X['col1'].max() - X['col1'].min())
print (X)

   col1      col2
0   100  0.545455
1    10  0.136364
2     1  0.095455
3    20  0.181818
4    10  0.136364
5   -20  0.000000
6   200  1.000000

Like @meW pointed in comments another solution is use MinMaxScaler:

from sklearn import preprocessing

min_max_scaler = preprocessing.MinMaxScaler()
X['col2'] = min_max_scaler.fit_transform(X[['col1']])
print (X)

   col1      col2
0   100  0.545455
1    10  0.136364
2     1  0.095455
3    20  0.181818
4    10  0.136364
5   -20  0.000000
6   200  1.000000