Creation of 1D, 2D, and 3D NumPy arrays

Python program for creation of NumPy array object

import numpy as np

arr = np.array([10,20,30,40,50])

print(arr)

print(type(arr))

Output.

 [10 20 30 40 50]

<class 'numpy.ndarray'>

Code explanation: In above code,

We have created an array object arr. It stores 5 integers. The values are stored in continuous memory allowing fast mathematical operations. Note that in the output we get the type of array as <class 'numpy.ndarray"> which indicates that it is an object,

Ex 10. Python program for creating 2D array object

import numpy as np

arr = np.array([[10,20,30],[40,50,60]])

print(arr)

print(type(arr))

Output

[[10 20 30]

 [40 50 60]]

<class 'numpy.ndarray'>

Code explanation: In above code,

The 2D Numpy array object is created in variable arr. It has 2 rows and 3 columns. The type returns the datatype of variable arr and it is ndarray. This is nothing but the array object.

Ex 11. Join - Basic Operations - NumPy (Python library)

In [20]: import numpy as np

a = np.array([[1,2,3],[4,5,6]])

print('First array:')

print(a)

b = np.array([[10,20,30], [40, 50, 60]])

print('Second array:')

print(b)

# both the arrays are of same dimensions

print("Joining the two arrays along axis 0:')

print(np.concatenate((a,b)))

print('Joining the two arrays along axis 1:')

print(np.concatenate ((a,b), axis = 1))

First array:

[[1 2 3]

 [4 5 6]]

second array:

 [[10 20 30]

 [40 50 60]]

Joining the two arrays along axis 0 :

[[1 2 3]

 [ 4 5 6]

 [10 20 30]

 [40 50 60]]

Joining the two arrays along axis 1:

[[ 1 2 3 10 20 30]

 [ 4 5 6 40 50 60]]

Ex 12. Join - Basic Operations - NumPy (Python library)

Python program for vertical join of two dimensional arrays

import numpy as np

arr1 = np.array([[10,20], [30,40]])

arr2 = np.array([[50,60],[70,80]])

result = = np.vstack((arr1,arr2))

print("Vertical Join: ",result)

Output

Vertical Join: [[10 20]

[30 40]

[50 60]

[70 80]]

Ex 13. Join - Basic Operations Python program - NumPy (Python library)

Python program for horizontal join of two dimensional arrays

import numpy as np

arr1 = np.array([[10,20],[30,40]])

arr2 = np.array([[50,60],[70,80]])

result = np.hstack((arr1, arr2))

print("Horizontal Join: ",result)

Output

Horizontal Join: [[10 20 50 60]

 [30 40 70 80]]

14. Split - Basic Operations Python program - NumPy (Python library)

Python program

import numpy as np

arr = np.array([10,20,30,40,50,60])

result = np.split(arr,3)

print(result)

Output

 [array([10, 20]), array([30, 40]), array([50, 60])]

The above code splits the array into 3 equal parts and each part gets 2 elements.

• np.array_split() : The array_split() allows uneven splitting when the array length isn't divisible by the number of parts. For an array that causes uneven split, the np.split( ) raises error but the np.array.split() works fine.

For example ‒

Ex 15. np.split(): Split - Basic Operations Python program - NumPy (Python library)

Python program

import numpy as np

am = np.array([10,20,30,40,50])

result = np.array_split(arr,3)

print(result)

Output

[array([10, 20]), array([30, 40]), array([50])]

Ex 16. np.array_split(): Split - Basic Operations Python program - NumPy (Python library)

Python program

import numpy as np

am = np.array([10,20,30,40,50])

result = np.array_split(arr,3)

print(result)

Output

[array([10, 20]), array([30, 40]), array([50])]

Ex 17. np.hsplit(): Split - Basic Operations Python program - NumPy (Python library)

Python program

import numpy as np

arr = np.array([[1,2,3,4],

[5,6,7,8],

[9,10,11,12],

[13,14,15,16]])

print(arr,"\n")

h_parts = np.hsplit(arr, 2)

count = 1

for part in h_parts:

   print("Part", count, ":\n", part, "\n")

   count += 1

Output

[[ 1 2 3 4]

 [ 5 6 7 8]

 [ 9 10 11 12]

 [13 14 15 16]]

Part 1:

 [[ 1 2 ]

  [ 5 6 ]

 [ 9 10]

 [13 14]]

Part 2 :

[[ 3 4]

 [ 7 8]

 [11 12]

 [15 16]]

Ex 18. np.vsplit(): Split - Basic Operations Python program - NumPy (Python library)

Python program

import numpy as np

arr = np.array([[1,2,3,4],

[5,6,7,8],

[9,10,11,12],

[13,14,15,16]])

print(arr, "\n")

v_parts = np.vsplit(arr, 2)

count = 1

for part in v_parts:

    print("Part", count, ":\n", part, "\n")

    count += 1

Output

[[ 1 2 3 4]

[ 5 6 7 8]

 [ 9 10 11 12]

[13 14 15 16]]

Part 1:

[[1 2 3 4]

[5 6 7 8]]

Part 2:

[[ 9 10 11 12]

[13 14 15 16]]

19. Search - Basic Operations Python program - NumPy (Python library)

Python program

import numpy as np

arr = np.array([10,20,30,40,20,50,20])

result = np.where(arr==20)

print("Index position of 20:",result)

Output

Index position of 20: (array([1, 4, 6], dtype=int64),)

It means, position of element 20 in the array is at index 1,4 and 6. Hence is the output.

20. Sort - Basic Operations Python program - NumPy (Python library)

 Python program

Ex 22.

Write a Python program to demonstrate the exponentiation operation on arrays using NumPy.

Solution:

In [2]: import numpy as np

a = np.array([1,2,3,4,5])

b = a ** 2

print("Exponentiation using ** operator ",b)

c = np.power(a,2)

print("Exponentiation using power() function ",c)

Exponentiation using ** operator [ 1 4 9 16 25]

Exponentiation using power() function [ 1 4 9 16 25]

Ex 23.

Write a Python program to demonstrate the modulus operation on arrays using NumPy.

Solution:

In [4]: import numpy as np

a = np.array([10,20,30,40])

b = np.array([2,3,7,9])

c = a % b

print("Modulus using % operator ",c)

d = np.mod(a,b)

print("Modulus using mod function",d)

Modulus using % operator [0 2 2 4]

Modulus using mod function [0 2 2 4]