In [1]:

    

import numpy as np


    

In [2]:

    

#creating array by providing numerical values
arr = np.array([[1,2,3,4],[5,6,7,8]])
arr


    

    
Out[2]:





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

In [3]:

    

#numpy supports multidimensional array
arr = np.arange(12)
arr


    

    
Out[3]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

In [4]:

    

#we can format the array to different dimensions
#Let's convert the arrray into 3 x 4
arr.reshape(3,4)
arr


    

    
Out[4]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

In [5]:

    

#The array is not reshaped as it returns a different objet
#To get what we intend let's assign the returned the object to arr itself
arr=arr.reshape(3,4)
arr


    

    
Out[5]:





array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

In [6]:

    

#iterating over the array
for x in np.nditer(arr):
    print(x)


    

    




0
1
2
3
4
5
6
7
8
9
10
11

In [7]:

    

#multiplication
arr*arr


    

    
Out[7]:





array([[  0,   1,   4,   9],
       [ 16,  25,  36,  49],
       [ 64,  81, 100, 121]])

In [8]:

    

#addition
arr+arr


    

    
Out[8]:





array([[ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])

In [9]:

    

#sum of all elements
arr.sum()


    

    
Out[9]:





66

In [10]:

    

#sum of each row separately
arr.sum(axis=0)


    

    
Out[10]:





array([12, 15, 18, 21])

In [11]:

    

#sum of each column separately
arr.sum(axis=1)


    

    
Out[11]:





array([ 6, 22, 38])

In [12]:

    

#slicing
arr[0:2]


    

    
Out[12]:





array([[0, 1, 2, 3],
       [4, 5, 6, 7]])

In [13]:

    

#last row
arr[-1]


    

    
Out[13]:





array([ 8,  9, 10, 11])