from sklearn import preprocessing
lab_encoder = preprocessing.LabelEncoder()
df['column'] = lab_encoder.fit_transform(df['column'])
sex = train_dataset['Sex'].replace(['female','male'],[0,1])
print(sex)
#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