One-Hot Encoding in Scikit-learn for only part of the DataFrame

问题

I am trying to use a decision tree classier on my data which looks very similar to the data in this tutorial: https://www.ritchieng.com/machinelearning-one-hot-encoding/

The tutorial then goes on convert the strings into numeric data:

X = pd.read_csv('titanic_data.csv')
X = X.select_dtypes(include=[object])
le = preprocessing.LabelEncoder()
X_2 = X.apply(le.fit_transform)

This leaves the DataFrame looking like this:

After this, the data is put through the OneHotEncoder and I assume can then be split and passed into a decision tree classier fairly easily.

The problem is that it appears to me that the original numeric data gets lots through this process of encoding. How can I keep or add in later the numeric data that was removed during the encoding process? Thanks!

回答1:

Actually there is a really simple solution - using pd.get_dummies()

If you have a Data Frame like the following:

so_data = {
    'passenger_id': [1,2,3,4,5],
    'survived': [1,0,0,1,0],
    'age': [24,25,68,39,5],
    'sex': ['female', 'male', 'male', 'female', 'female'],
    'first_name': ['Joanne', 'Mark', 'Josh', 'Petka', 'Ariel']
}
so_df = pd.DataFrame(so_data)

which looks like:

    passenger_id    survived    age   sex       first_name
0              1           1    24  female        Joanne
1              2           0    25  male          Mark
2              3           0    68  male          Josh
3              4           1    39  female        Petka
4              5           0    5   female        Ariel

You can just do: