Python Feature Engineering Cookbook

How to do it...

Let's begin with the recipe by making some imports and preparing the data:

1. Import pandas and the required functions and classes:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom feature_engine.categorical_encoders import CountFrequencyCategoricalEncoder

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 count the number of observations per category of the A7 variable and capture it in a dictionary:

count_map = X_train['A7'].value_counts().to_dict()

To replace by frequency instead of count, we should divide ...