Contenido del Curso
ML Introduction with scikit-learn
ML Introduction with scikit-learn
OrdinalEncoder
The next problem we will solve is categorical data. Recall that there are two types of categorical data.
Ordinal data follows some natural order, while nominal does not.
Since there is a natural order, we can encode categories to the numbers in that order.
For example, we would encode the 'rate' column containing 'Terrible', 'Bad', 'OK', 'Good', and 'Great' values like:
- 'Terrible' – 0;
- 'Bad' – 1;
- 'OK' – 2;
- 'Good' – 3;
- 'Great' – 4.
To encode ordinal data, OrdinalEncoder
is used. It just encodes the categories to 0, 1, 2, ...
Here is an image showing how it works.
OrdinalEncoder
is easy to use like any other transformer. The only difficulty is to specify the categories
argument correctly.
Let's look at the example of use. We have a dataset (not the Penguins dataset) with the 'education'
column. Let's look at its unique values.
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/adult_edu.csv') print(df['education'].unique())
We need to create a list of ordered categorical values, in this case, from 'HS-grad' to 'Doctorate'.
import pandas as pd from sklearn.preprocessing import OrdinalEncoder # Load the data and assign X, y variables df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/adult_edu.csv') y = df['income'] # 'income' is a target in this dataset X = df.drop('income', axis=1) # Create a list of categories so HS-grad is encoded as 0 and Doctorate as 6 edu_categories = ['HS-grad', 'Some-college', 'Assoc', 'Bachelors', 'Masters', 'Prof-school', 'Doctorate'] # Initialize an OrdinalEncoder instance with the correct categories ord_enc = OrdinalEncoder(categories=[edu_categories]) # Transform the 'education' column and print it X['education'] = ord_enc.fit_transform(X[['education']]) print(X['education'])
Note
OrdinalEncoder
is mostly used to transform the features (X
variable). And theX
variable usually is a DataFrame containing more than 1 column.
Because of that, thecategories
argument allows specifying the list of categories for each column, e.g.,categories=[col1_categories, col2_categories]
.
And if you want to transform only 1 column, you should still pass a list containing another list, e.g.,categories=[col1_categories]
.
That's also the reason the.fit_transform()
method expects the DataFrame and doesn't work with Series, so you need to passdf[['column']]
to transform only one column.
¡Gracias por tus comentarios!