Python Feature Engineering Cookbook

How to do it...

Let's first import the necessary Python libraries and get the dataset ready:

1. Import the required Python library, function, and class:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom category_encoders import BinaryEncoder

2. Let's load the dataset and divide it into train and test sets:

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

3. Let's inspect the unique categories in A7:

X_train['A7'].unique()

We can see in the output of the preceding code block that A7 has 10 different categories:

array(['v', 'ff', 'h', 'dd', 'z', 'bb', 'j', 'Missing', 'n', 'o'], dtype=object)