One hot Encoding with nominal categorical features in Python?
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One hot Encoding with nominal categorical features in Python?

One hot Encoding with nominal categorical features in Python?

One hot Encoding with nominal categorical features in Python

Recipe Objective

We can not pass categorical variables in models so how to handle categorical variables. We can use one hor encoding to do this.

So this is the recipe on how we can do One hot Encode with nominal categorical features in Python.

Step 1 - Import the library

import numpy as np from sklearn.preprocessing import LabelBinarizer

We have only imported numpy and LabelBinarizer which is needed.

Step 2 - Creating an array

We have created an array on which we will perform the operation. x = np.array([["Texas"], ["California"], ["Texas"], ["Delaware"], ["Texas"]])

Step 3 - One hot encoding

We have created an object LabelBinarizer to change the catergorical variables. We have use fit_transform to change the variables and printed the class. one_hot = LabelBinarizer() print(one_hot.fit_transform(x)) print(one_hot.classes_) So the output comes as

[[0 0 1]
 [1 0 0]
 [0 0 1]
 [0 1 0]
 [0 0 1]]

["California" "Delaware" "Texas"]

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