TargetEncode.py

import pandas as pd
from sklearn import preprocessing

vehiclerDF = pd.DataFrame({'id':[101, 102, 103, 104, 105, 106, 107, 108], 
                          'vehicle':['Car', 'Minivan', 'SUV', 'Car', 'Car', 'Minivan','Car', 'Minivan'],
                          'label':['Yes', 'Yes', 'Yes', 'No', 'Yes', 'No','Yes', 'No']})

# Encode label (target)
labelEncode = preprocessing.LabelEncoder()
vehiclerDF['label'] = labelEncode.fit_transform(vehiclerDF['label']) 

# Group by category and calculate "mean" per item in the category
means = vehiclerDF.groupby('vehicle').label.mean()

# Map mean values against each respective item in the category
vehiclerDF['vehicleTargetRatio'] = vehiclerDF['vehicle'].map(means)

# Cleanup unwanted features
vehiclerDF.drop(['vehicle'], axis=1, inplace=True)
print(vehiclerDF)