import seaborn as sns
df = sns.load_dataset('iris')
df['species'] = df['species'].astype('category').cat.codes
df.head(3)
'''sepal_length sepal_width petal_length petal_width species
5.1 3.5 1.4 0.2 0
4.9 3.0 1.4 0.2 0
4.7 3.2 1.3 0.2 0'''from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
companydata.ShelveLoc = le.fit_transform(companydata.ShelveLoc)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)
# Import label encoder
from sklearn import preprocessing
# label_encoder object knows how to understand word labels.
label_encoder = preprocessing.LabelEncoder()
# Encode labels in column 'species'.
df['species']= label_encoder.fit_transform(df['species'])
df['species'].unique()