Array

• An indexed collection of homogenous data elements is nothing but array.
• It is the most commonly used concept in programming language like C/C++, java … etc.
• By default arrays concept is not available in python, instead we can use List.
• In python, we can create arrays in the following 2 ways:
  • Using array module.
  • Using numpy module.

ndarray in numpy

• In numpy data is stored in the form of array.
• The arrays which are created by using numpy are called ndarray.
• ndarray ⇒ n-dimensional array or numpy array.

Python list vs numpy array

• Python lists and numpy arrays are both container data types that can hold a collection of items. They are used in different contexts and have different characteristics, making each of them suitable for particular tasks.

Similarities

• Both can be used to store data.
• The order will be preserved in both. Hence indexing and slicing concepts are applicable.
• Both are mutable, i.e. we can change the content

Differences

• List is python’s inbuilt type. But we have to install and import numpy explicitly.
• List can contain heterogeneous elements. But array contains only homogeneous elements.
• On list, we cannot perform vector operations. But on ndarray we can perform vector operations.
• Arrays consume less memory than list.
• Arrays are superfast when compared with list.
• Numpy arrays are more convenient to use while performing complex mathematical operations.

Example

• Write a program to show in list we cannot perform vector operations. But on ndarray we can perform vector operations.

import numpy as np
my_list = [10,20,30,40]
my_array = np.array([10,20,30,40])
#print(my_list+2)
print(my_array+2)

Output

#TypeError: can only concatenate list (not "int") to list
[12 22 32 42]

Example

• Write a program to show arrays consume less memory than list.

import numpy as np
import sys
my_list = [10,20,30,40,50,60,70,80,90,100,10,20,30,40,50,60,70,80,90,100]
my_array = np.array([10,20,30,40,50,60,70,80,90,100,10,20,30,40,50,60,70,80,90,100])
print('The Size of list => ',sys.getsizeof(my_list))
print('The Size of ndarray => ',sys.getsizeof(my_array))

Output

The Size of list => 216
The Size of ndarray => 184

Example

• Write a program to show arrays are superfast when compared with list.

import numpy as np
from datetime import datetime
array1 = np.array([10,20,30])
array2 = np.array([1,2,3])

#traditional python code
def dot_product(a,b):
 result = 0
 for i,j in zip(a,b):
   result = result + i*j
 return result

before = datetime.now()
for i in range(1000000):
 dot_product(array1,array2)
after = datetime.now()
print('The Time taken by traditonal python:',after-before)

#numpy library code
before = datetime.now()
for i in range(1000000):
 np.dot(array1,array2)
after = datetime.now()
print('The Time taken by Numpy Library:',after-before)

Output

The Time taken by traditonal python: 0:00:01.580073
The Time taken by Numpy Library: 0:00:01.031358

Ungraded Questions

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