categorical.py

import pandas as pd 
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from data_description import DataDescription
class Categorical:
    tasks = [
        '\n1. Show Categorical Columns',
        '2. Performing One Hot encoding',
        '3. Show the Dataset'
    ]
    def __init__(self, data):
        self.data = data
    def categoricalColumn(self):
        print('\n{0: <20}'.format("Categorical Column") + '{0: <5}'.format("Unique Values"))
        for column in self.data.select_dtypes(include="object"):
            print('{0: <20}'.format(column) + '{0: <5}'.format(self.data[column].nunique()))
    def encoding(self):
        categorical_columns = self.data.select_dtypes(include="object")
        while(1):
            column = input("\nWhich column would you like to one hot encode?[enter -1 to go back]  ").lower()
            if column == "-1":
                break
            if column in categorical_columns:
                self.data = pd.get_dummies(data=self.data, columns = [column])
                print("Encoding is done.......")
                choice = input("Are there more columns to be encoded?(y/n)  ")
                if choice == "y" or choice == "Y":
                    continue
                else:
                    self.categoricalColumn()
                    break
            else:
                print("Wrong column Name. Try Again...")
    def categoricalMain(self):
        while(1):
            print("\nTasks")
            for task in self.tasks:
                print(task)
            while(1):
                try:
                    choice = int(input(("\n\nWhat you want to do? [enter -1 to go back]  ")))
                except ValueError:
                    print("Integer Value required. Try again...")
                    continue
                break
            if choice == -1:
                break
            elif choice == 1:
                self.categoricalColumn()
            elif choice == 2:
                self.categoricalColumn()
                self.encoding()
            elif choice == 3:
                DataDescription.showDataset(self)
            else:
                print("\nWrong choice!! Try again...")
        return self.data