Is this valid way of doing one hot encoding ? Since it is not showing any created dummies .

Question

# # Encoding categorical data & Feature Scaling

# Select the categorical columns
categorical_subset = credit[['Term', 'Years in current job', 'Home Ownership', 'Purpose']]

# One hot encode
categorical_subset = pd.get_dummies(categorical_subset)

# Join the dataframe in credit_train
# Make sure to use axis = 1 to perform a column bind
# First I will drop the 'old' categorical datas and after I will join the 'new' one.

credit.drop(labels=['Term', 'Years in current job', 'Home Ownership', 'Purpose'], axis=1, inplace=True)
credit = pd.concat([credit, categorical_subset], axis = 1)

pd.get_dummies(categorical_subset) This statement is not showing any dummy ... it only shows the original columns.... ? Is it also okay to do OneHotEncoding in this way...?