```
from sklearn.preprocessing import LabelEncoder as LE, OneHotEncoder as OHE
import numpy as np

a = np.array([[0,1,100],[1,2,200],[2,3,400]])

oh = OHE(categorical_features=[0,1])
a = oh.fit_transform(a).toarray()

```

Let's assume first and second column are categorical data. This code does one hot encoding, but for the regression problem, I would like to remove first column after encoding categorical data. In this example, there are two and I could do it manually. But what if you have many categorical features, how would you solve this problem?