Python Feature Engineering Cookbook

How it works...

In this recipe, we performed a one-hot encoding of categorical variables using pandas and scikit-learn.

We loaded the dataset and separated it into train and test sets using scikit-learn's train_test_split() function. Next, we used pandas' get_dummies() function on the A4 variable, setting drop_first=True to drop the first binary variable and hence obtain k-1 binary variables. Next, we used get_dummies() on all of the categorical variables of the dataset, which returned a dataframe with binary variables representing the categories of the different features.

One-hot encoding expands the feature space. We created, from 9 original categorical variables, 36 binary ones.

Finally, we performed one-hot encoding using OneHotEncoder() ...