NumPy Array Indexing and Slicing of Python NumPy Module

NumPy Array Indexing and Slicing of Python NumPy Module. The following shows various universal indexing and slicing technic of the NumPy array of Python NumPy module. 

 

import numpy as np

 

Code:

a = np.arange(10)**3
a

Output:

array([  0,   1,   8,  27,  64, 125, 216, 343, 512, 729])

 

Code:

a[2]

Output:

8

 

Code:

a[2:5]

Output:

array([ 8, 27, 64])

 

Code:

a[:6:2] = -1000
a

Output:

array([-1000,     1, -1000,    27, -1000,   125,   216,   343,   512,   729])

 

Code:

a[ : :-1]

Output:

array([  729,   512,   343,   216,   125, -1000,    27, -1000,     1, -1000])

 

Code:

def f(x,y):
    return 10*x+y

b = np.fromfunction(f,(5,4),dtype=int)
b

Output:

array([[ 0,  1,  2,  3],
       [10, 11, 12, 13],
       [20, 21, 22, 23],
       [30, 31, 32, 33],
       [40, 41, 42, 43]])

 

Code:

b[2,3]

Output:

23

 

Code:

b[0:5, 1] 

Output:

array([ 1, 11, 21, 31, 41])

 

Code:

b[0:3, 2] 

Output:

array([ 2, 12, 22])

 

Code:

b[ : ,1] 

Output:

array([ 1, 11, 21, 31, 41])

 

Code:

b[1:3, : ]

Output:

array([[10, 11, 12, 13],
       [20, 21, 22, 23]])

 

Code:

b[-1]

Output:

array([40, 41, 42, 43])

 

Code:

c = np.array( [[[  0,  1,  2],               # a 3D array (two stacked 2D arrays)
                [ 10, 12, 13]],
               [[100,101,102],
                [110,112,113]]])

c.shape

Output:

(2, 2, 3)

 

Code:

c[1,...] # same as c[1,:,:] or c[1]

Output:

array([[100, 101, 102],
       [110, 112, 113]])

 

Code:

c[...,2] # same as c[:,:,2]

Output:

array([[  2,  13],
       [102, 113]])