Numpy Overview

Scientific computing package for Python
Arrays are the fundamental elements
Methods to manipulate and perform mathematical operations on arrays and matrices
Documentation: https://docs.scipy.org/doc/

Import the Numpy Package



In [33]:

    
import numpy as np



In [2]:

    
A = np.array([[1,2,3]]); print(A)









    



[[1 2 3]]



In [3]:

    
B = np.array([[1,2,3],[4,5,6],[7,8,9]]); print(B)









    



[[1 2 3]
 [4 5 6]
 [7 8 9]]



In [4]:

    
C = np.zeros((2,1)); print(C)









    



[[ 0.]
 [ 0.]]



In [5]:

    
D = np.ones((1,3)); print(D)









    



[[ 1.  1.  1.]]



In [6]:

    
E = np.random.randn(3,3); print(E)









    



[[ 0.71722718  0.62570159  0.39517151]
 [ 0.91379584 -0.41934408  0.66504493]
 [-1.50850069  0.2865281  -0.8727147 ]]



In [7]:

    
print(B)









    



[[1 2 3]
 [4 5 6]
 [7 8 9]]



In [41]:

    
B[0]  #first row









    Out[41]:





array([1, 2, 3])



In [42]:

    
B[:,0] #first column









    Out[42]:





array([1, 4, 7])



In [9]:

    
B[0,0]









    Out[9]:





1



In [10]:

    
B[2,2]









    Out[10]:





9



In [11]:

    
B[:,0]









    Out[11]:





array([1, 4, 7])



In [12]:

    
print(B.dtype)
print(B.shape)
print(B.ndim)
print(B.size)









    



int32
(3, 3)
2
9



In [13]:

    
B.tolist()









    Out[13]:





[[1, 2, 3], [4, 5, 6], [7, 8, 9]]



In [14]:

    
B.T









    Out[14]:





array([[1, 4, 7],
       [2, 5, 8],
       [3, 6, 9]])



In [15]:

    
B.reshape(1,9)









    Out[15]:





array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])



In [16]:

    
B.flatten()









    Out[16]:





array([1, 2, 3, 4, 5, 6, 7, 8, 9])



In [17]:

    
A.nonzero()









    Out[17]:





(array([0, 0, 0], dtype=int64), array([0, 1, 2], dtype=int64))



In [18]:

    
A









    Out[18]:





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



In [19]:

    
A.min()









    Out[19]:





1



In [20]:

    
A.max()









    Out[20]:





3



In [21]:

    
A.sum()









    Out[21]:





6



In [22]:

    
A.mean()









    Out[22]:





2.0



In [23]:

    
A.var()









    Out[23]:





0.66666666666666663



In [24]:

    
A.std()









    Out[24]:





0.81649658092772603



In [25]:

    
A









    Out[25]:





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



In [26]:

    
A*A









    Out[26]:





array([[1, 4, 9]])



In [27]:

    
A**A









    Out[27]:





array([[ 1,  4, 27]], dtype=int32)



In [28]:

    
A/A









    Out[28]:





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



In [29]:

    
A-A









    Out[29]:





array([[0, 0, 0]])



In [30]:

    
A+A









    Out[30]:





array([[2, 4, 6]])



In [31]:

    
A<B









    Out[31]:





array([[False, False, False],
       [ True,  True,  True],
       [ True,  True,  True]], dtype=bool)



In [32]:

    
A==B









    Out[32]:





array([[ True,  True,  True],
       [False, False, False],
       [False, False, False]], dtype=bool)