Day #309 - Handle Categorical Columns

Have a Great, Peaceful and Successful 2020
This post is on Handling Categorical Columns

	import pandas as pd
	#Define Data Frames
	Data = {'Location': ['Singapore', 'India', 'Japan', 'China','Korea'],
	'avgage': [22,38,26,35,22],
	'Education': ['UG','PG','Phd','UG','PG']
	}
	Dataset = pd.DataFrame(Data)
	#Categorize Location
	location = Dataset['Location']
	catlocation = pd.get_dummies(location)
	print(catlocation)
	#Categorize Education
	education = Dataset['Education']
	cateducation = pd.get_dummies(education)
	print(cateducation)
	#Standardize Avg Age
	from sklearn import preprocessing
	age = Dataset['avgage'].values
	min_max_scaler = preprocessing.MinMaxScaler()
	age_scaled = min_max_scaler.fit_transform(age.reshape(-1, 1))
	agedf = pd.DataFrame(age_scaled)
	print(agedf)
	#Merge all the data
	frames = [catlocation,cateducation,agedf]
	#Merge three frames horizontally
	merged_data = pd.concat(frames, axis=1)
	print(merged_data)

view raw catcolumns.py hosted with ❤ by GitHub

Happy Learning!!!