MATLAB vs Python Syntax (I)

MATLAB	Python (Numpy)
I/O
load 'hello.txt' X	X=pd.read_csv('hello.txt, header=None)
save 'hello.txt' X -ascii	np.savetxt('hello.txt', X)
-	import scipy.io as sio
-	test=sio.loadmat('test.mat')
Data Creation
A=[1 2; 3 4; 5 6]	A=np.array([[1,2], [3,4], [5,6]])
v=[1 2 3]	v=np.array([1, 2, 3])
v=[1; 2; 3]	v=np.array([[1], [2], [3]])
v=1: 0.1: 2	v=np.arange(1, 2.1, 0.1)
c=2*ones(2,3)	v=2.*np.ones((2, 3))
w=zeros(1,3)	w=np.zeros(3).T
r=rand(1,3)	r=np.random.rand(3,4)
I=eye(4)	I=np.eye(4)
size(A) #3 2	A.shape
length(v) #3	v.size
m=size(A, 1)	m=A.shape[0]
Data Extraction
A(3, 2) #6	A[2, 1]
A(2,:)	A[1]
A([1 3], :)	A[[0, 2]]
A(2:end, 1)	A[1:, 0]
R=rand(4,5)	R=np.random.rand(4,5)
R(R(:,3)>0.5, [2,4])	R[R[:2]>0.5][:,[1,3]]
pos=find(p>0.5)	pos=np.where(p>0.5)
X1=X(pos, :)	X1=X[pos]
X1=X(p>0.5, :)	X1=X[p>0.5, :]
Concatenate Data
A=[A, [101; 100; 102]]	A=np.hstack([A, np.array([[101], [102], [103]]) ])
-	A=np.c_[A, np.array([[101], [102], [103]])]
X=[ones(m,1), X]	np.c_[np.ones(m), X]
Basic Operation
a == b	a == b
a ~= b	a != b
a && b	a and b
a \|\| b	a or b
xor(a,b)	a ^ b
2^3	2**3
Matrix Operation
A * B #dot product	A.dot(B)
A' #transpose	A.T
A' + B	A.T + B
A .* B #element-wise	A * B
A .^ 2	A ** 2
1 ./ A	1. / A
log(A)	np.log(A)
exp(A)	np.exp(A)
A * v #result mx1	A.dot(v) #result 1D array
pinv(A) #inverse	np.linalg.pinv(A)