Day #37 - Numpy Learnings - Matrices

	import pandas as pd
	import numpy as np
	df = pd.DataFrame({'A':[1.1,2.2,3.1],'B':[5.2,6.1,7.1],'C':[5.4,6.6,7.4]})
	print(df)
	#Tip #1
	#Create matrix of float values
	data_intermediate = df.astype(float)
	data_matrix = np.matrix(data_intermediate)
	print('matrix')
	print(data_matrix)
	#Tip #2 Compute Transpose
	print('Transpose matrix')
	print(np.transpose(data_matrix))
	#Tip #3 - Matrix Inverse
	print('Inverse matrix')
	print(data_matrix.I)
	#Tip #4 - Identity Matrix
	print('Identity Matrix')
	print(np.identity(3))
	print('Identity Matrix X Data ')
	print(np.identity(3)*data_matrix)
	#Tip #5 - Eigen Values
	print('Eigen Values')
	print(np.linalg.eigvals(data_matrix))
	#Tip #6 - Eigen Vectors
	w, v= np.linalg.eig(data_matrix)
	print('Eigen Vector')
	print(v)
	#Tip #7 - svd
	print('SVD')
	print(np.linalg.svd(data_matrix))

view raw numpybasics.py hosted with ❤ by GitHub

Happy Learning!!!