One-hot-encoding multiple columns in sklearn and naming columns

Question

I have the following code to one-hot-encode 2 columns I have.

# encode city labels using one-hot encoding scheme
city_ohe = OneHotEncoder(categories=\'auto\'         


        

Answer 1

Why don't you take a look at pd.get_dummies? Here's how you can encode:

df['city'] = df['city'].astype('category')
df['phone'] = df['phone'].astype('category')
df = pd.get_dummies(df)

Answer 2

You you are almost there... Like you said you can add all the columns you want to encode in fit_transform directly.

ohe = OneHotEncoder(categories='auto')
feature_arr = ohe.fit_transform(df[['phone','city']]).toarray()
feature_labels = ohe.categories_

And then you just need to do the following:

feature_labels = np.array(feature_labels).ravel()

Which enables you to name your columns like you wanted:

features = pd.DataFrame(feature_arr, columns=feature_labels)