NumPy is the most important scientific programming Python package.



In [1]:

    
import numpy as np
np.eye(3) # identity matrix









    Out[1]:





array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

NumPy comes with a powerful and expressive array type.



In [2]:

    
# Defining an array
my_array = np.array([[0, 1], [2, 3]])
my_array









    Out[2]:





array([[0, 1],
       [2, 3]])



In [3]:

    
# Slicing
print my_array[0, :]
print my_array[1, :]
print my_array[:, 0]
print my_array[:, 1]









    



[0 1]
[2 3]
[0 2]
[1 3]



In [4]:

    
# Multiplication
my_array.dot(my_array)









    Out[4]:





array([[ 2,  3],
       [ 6, 11]])



In [5]:

    
# Solving a linear equation
a = np.array([[3,1], [1,2]])
b = np.array([9,8])
print np.linalg.solve(a, b)









    



[ 2.  3.]



In [6]:

    
# Finding the pseudoinverse
np.linalg.pinv(my_array)









    Out[6]:





array([[ -1.50000000e+00,   5.00000000e-01],
       [  1.00000000e+00,   5.55111512e-17]])



In [7]:

    
# Transposing
my_array.T









    Out[7]:





array([[0, 2],
       [1, 3]])



In [8]:

    
# Vector operations
my_vector = np.array([1, 2, 3])
print my_vector

# Dot product
print my_vector.dot(my_vector.T)

# Cross product
print np.cross(my_vector.T, my_vector)