Active August 25, 2020    /    Viewed 2312    /    Comments 0    /    Edit

Examples of how to transform (encode) a qualitative (categorical) variable into a quantitative variable with scikit learn in python ?

Input matrix

Let's consider the following input matrix X:

from sklearn import preprocessing

import numpy as np

X = np.array(('A','C','B','A','C','D','A'))

of shape

print(X.shape)

(7,)

that can be reshaped:

X = X.reshape(-1,1)

returns

print(X.shape)

(7, 1)

Encoding the elements of matrix X using the function OrdinalEncoder

To encode the elements of matrix X a solution is to use OrdinalEncoder:

enc = preprocessing.OrdinalEncoder(categories='auto')

enc.fit(X)

print( enc.transform(X) )

returns

[[0.]
[2.]
[1.]
[0.]
[2.]
[3.]
[0.]]

Encoding the elements of matrix X using the function OneHotEncoder

Another solution to encode the elements of matrix X using the function OneHotEncoder

enc = preprocessing.OneHotEncoder(categories='auto')

enc.fit(X)

print( enc.transform(X) )

returns

(0, 0)    1.0
(1, 2)    1.0
(2, 1)    1.0
(3, 0)    1.0
(4, 2)    1.0
(5, 3)    1.0
(6, 0)    1.0

To get a matrix just use toarray() :

print( enc.transform(X).toarray() )

gives here

[[1. 0. 0. 0.]
[0. 0. 1. 0.]
[0. 1. 0. 0.]
[1. 0. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]
[1. 0. 0. 0.]]  Daidalos

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Amount