CODEWITHSUNDEEP
pythonpandasscikit-learnsklearn-pandas
melik
melik

Reputation: 1332

Group by MinMaxScaler in pandas dataframe

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.

Upvotes: 5

Views: 5180

Answers (1)

Shubham Sharma
Shubham Sharma

Reputation: 71689

Approach 1

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)

Approach 2

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_)

Result

    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

Upvotes: 7

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