Accessing Data Along Multiple Dimensions Arrays in Python Numpy

NumPy (Numerical Python) is a Python library that comprises of multidimensional arrays and numerous functions to perform various mathematical and logical operations on them. NumPy also consists of various functions to perform linear algebra operations and generate random numbers. NumPy is often used along with packages like SciPy and Matplotlib for technical computing.
An n-dimensional (multidimensional) array has a fixed size and contains items of the same type. the contents of the multidimensional array can be accessed and modified by using indexing and slicing the array as desired. For accessing elements of an array we need to first import the library: 
 

import numpy as np

We can use Integer Indexing to access elements of data. We can also perform Slicing to access sub-sequences of data.
Example 1:
 

# 1-dimensional array
array1D = np.array([1, 2, 3, 4, 5])

print(array1D)

# to access elements using positive
# index
print("\nusing positive index :" +str(array1D[0]))
print("using positive index :" +str(array1D[4]))

# negative indexing works in opposite
# direction
print("\nusing negative index :" +str(array1D[-5]))
print("using negative index :" +str(array1D[-1]))

Output :
 

[1 2 3 4 5]

using positive index :1
using positive index :5

using negative index :5
using negative index :1

Example 2: 
 

# 2-dimensional array 
array2D = np.array([[93,  95],
                    [84, 100],
                    [99,  87]])

print(array2D)
print("shape :" +str(array2D.shape))

print("\npositive indexing :" +str(array2D[1, 0]))
print("negative indexing :" +str(array2D[-2, 0]))

print("\nslicing using positive indices :" +str(array2D[0:3, 1]))
print("slicing using positive indices :" +str(array2D[:, 1]))
print("slicing using negative indices :" +str(array2D[:, -1]))

Output :
 

[[ 93  95]
 [ 84 100]
 [ 99  87]]
shape :(3, 2)

positive indexing :84
negative indexing :84

slicing using positive indices :[ 95 100  87]
slicing using positive indices :[ 95 100  87]
slicing using negative indices :[ 95 100  87]

Example 3: 
 

# 3-dimensional array 
array3D = np.array([[[ 0,  1,  2],
                     [ 3,  4,  5],
                     [ 6,  7,  8]],
 
                    [[ 9, 10, 11],
                     [12, 13, 14],
                     [15, 16, 17]],

                    [[18, 19, 20],
                     [21, 22, 23],
                     [24, 25, 26]]])

print(array3D)
print("shape :" +str(array3D.shape))

print("\naccessing element :" +str(array3D[0, 1, 0]))
print("accessing elements of a row and a column of an array:"
      +str(array3D[:, 1, 0]))
print("accessing sub part of an array :" +str(array3D[1]))

Output :
 

[[[ 0  1  2]
  [ 3  4  5]
  [ 6  7  8]]

 [[ 9 10 11]
  [12 13 14]
  [15 16 17]]

 [[18 19 20]
  [21 22 23]
  [24 25 26]]]
shape :(3, 3, 3)
accessing element :3
accessing elements of a row and a column of an array:[ 3 12 21]
accessing sub part of an array :[[ 9 10 11]
 [12 13 14]
 [15 16 17]]