Python Feature Engineering Cookbook

How to do it...

Let's first make the necessary imports and get the data ready:

1. Import pandas and the required function and class from scikit-learn:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import OneHotEncoder

2. Let's load the Credit Approval dataset:

data = pd.read_csv('creditApprovalUCI.csv')

3. Let's separate the data into train and test sets:

X_train, X_test, y_train, y_test = train_test_split( data.drop(labels=['A16'], axis=1), data['A16'], test_size=0.3,     random_state=0)

4. Let's inspect the unique categories of the A4 variable:

X_train['A4'].unique()

We see the unique values of A4 in the output of the preceding step:

array(['u', 'y', 'Missing', 'l'], dtype=object)

5. Let's encode ...