PYTHON

label encoder python

from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()

# apply on df
df['status'] = encoder.fit_transform(df['status'])
# can allso use the pandas.map
df['status'] = df['status'].map(lambda x: 1 if x=='Placed' else 0)

#apply on np.array
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1])], remainder='passthrough')
X = np.array(ct.fit_transform(X))

Label Encoding in python

##We apply Label Encoding on black Friday dataset on the target column which is Species. It contains three species Iris-setosa, Iris-versicolor, Iris-virginica. 

# Import libraries
import numpy as np
import pandas as pd
 
# Importing dataset
df = pd.read_csv('../../data/blackFriday.csv')

#Cheking out the unique values in your dataset
df['Age'].unique()

# Import label encoder
from sklearn import preprocessing
 
# label_encoder object knows how to understand word labels.
label_encoder = preprocessing.LabelEncoder()
 
# Encode labels in column 'Age'.
df['Age']= label_encoder.fit_transform(df['Age'])
 
df['Age'].unique()

how to use label encoding in python

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

obj_df["body_style_cat"] = obj_df["body_style"].cat.codes
obj_df.head()