One-Hot Encoder

When it comes to nominal values, handling them is a bit more complex.

Let's consider a feature containing ordinal data, such as user ratings. Its values range from 'Terrible' to 'Great'. It makes sense to encode these ratings as numbers from 0 to 4 because the ML model will recognize the inherent order.

Now, consider a feature labeled 'city' with five distinct cities. Encoding them as numbers from 0 to 4 would mistakenly imply a logical order to the ML model, which doesn’t actually exist. Therefore, a more suitable approach is to use one-hot encoding, which avoids implying any false order.

To encode nominal data, the OneHotEncoder transformer is used. It creates a column for each unique value. Then for each row, it sets 1 to the column of this row's value and 0 to other columns.

What was originally 'NewYork' now has 1 in the 'City_NewYork' column and 0 in other City_ columns.

Let's use OneHotEncoder on our penguins dataset! There are two nominal features, 'island' and 'sex' (not counting 'species', we will learn how to deal with target encoding in the next chapter).


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import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed.csv')

print('island: ', df['island'].unique())
print('sex: ', df['sex'].unique())

To use OneHotEncoder, you just need to initialize an object and pass columns to the .fit_transform() like with any other transformer.


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import pandas as pd
from sklearn.preprocessing import OneHotEncoder

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed.csv')
# Assign X, y variables
y = df['species']
X = df.drop('species', axis=1)
# Initialize an OneHotEncoder object
one_hot = OneHotEncoder()
# Print transformed 'sex', 'island' columns
print(one_hot.fit_transform(X[['sex', 'island']]).toarray())

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 6