I would like to apply minmax scaler to column X2 and X3 in dataframe df and add columns X2_Scale and X3_Scale for each month.
df = pd.DataFrame({
'Month': [1,1,1,1,1,1,2,2,2,2,2,2,2],
'X1': [12,10,100,55,65,60,35,25,10,15,30,40,50],
'X2': [10,15,24,32,8,6,10,23,24,56,45,10,56],
'X3': [12,90,20,40,10,15,30,40,60,42,2,4,10]
})
Below code is what I tried but got en error.
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
cols = df.columns[2:4]
df[cols + 'scale'] = df.groupby('Month')[cols].scaler.fit_transform(df[cols])
How can I do this? Thank you.
Group and transform the columns X2 and X3 using a scaling function which applies the min-max scaling transformation and returns the scaled values
def scale(X):
X_ = np.atleast_2d(X)
return pd.DataFrame(scaler.fit_transform(X_), X.index)
df[cols + '_scale'] = df.groupby('Month')[cols].apply(scale)
Lets stick to the basics and calculate the min, max values from corresponding columns X2 and X3 for each group, then apply the scaling formula to the columns using the calculated min-max values
g = df.groupby('Month')[cols]
min_, max_ = g.transform('min'), g.transform('max')
df[cols + '_scale'] = (df[cols] - min_) / (max_ - min_)
Month X1 X2 X3 X2_scale X3_scale
0 1 12 10 12 0.153846 0.025000
1 1 10 15 90 0.346154 1.000000
2 1 100 24 20 0.692308 0.125000
3 1 55 32 40 1.000000 0.375000
4 1 65 8 10 0.076923 0.000000
5 1 60 6 15 0.000000 0.062500
6 2 35 10 30 0.000000 0.482759
7 2 25 23 40 0.282609 0.655172
8 2 10 24 60 0.304348 1.000000
9 2 15 56 42 1.000000 0.689655
10 2 30 45 2 0.760870 0.000000
11 2 40 10 4 0.000000 0.034483
12 2 50 56 10 1.000000 0.137931