# get all categorical columns in the dataframe
catCols = [col for col in df1.columns if df1[col].dtype=="O"]

from sklearn.preprocessing import LabelEncoder

lb_make = LabelEncoder()

for item in catCols:
    df1[item] = lb_make.fit_transform(df1[item])

from sklearn.preprocessing import LabelEncoder

lb_make = LabelEncoder()
obj_df["make_code"] = lb_make.fit_transform(obj_df["make"])
obj_df[["make", "make_code"]].head(11)

#this will label as one hot vectors (origin is split into 3 columns - USA, Europe, Japan and any one place will be 1 while the others are 0)
dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})

pd.get_dummies(obj_df, columns=["body_style", "drive_wheels"], prefix=["body", "drive"]).head()

obj_df["body_style"] = obj_df["body_style"].astype('category')
obj_df.dtypes