encoders.py

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder 
from category_encoders import BinaryEncoder  
from category_encoders import TargetEncoder 

#Label Encoder
le = LabelEncoder() 
df['columnName'] = le.fit_transform(df['columnName'])

#Ordinal Encoder
ord = OrdinalEncoder() 
df["columnName"] = ord.fit_transform(df["columnName"]

#Frequency Encoder
fq = df.groupby('columnName').size()/len(df)    
df.loc[:, "{}_freq_encode".format('columnName')] = df['columnName'].map(fq)   
df = df.drop(['columnName'], axis = 1)  
                                
#Binary Encoder
encoder = BinaryEncoder(cols = ['columnName'])  
newdata = encoder.fit_transform(df['columnName']) 
                                
#One hot encoder                                
df = pd.get_dummies(df, prefix = ['OneHot'], columns = ['columnName'])
                                    
#Target Encoders
TargetEnc = TargetEncoder()
values = TargetEnc.fit_transform(X = df.columnName, y = df.Target)
df = pd.concat([df, values], axis = 1)