NumPy array over Python list

Now, the question arises what is so special about NumPy array ?

The most important benefits of using it are :

• It consumes less memory
• It is fast as compared to it
• It is convinient

Now, lets compare NumPy array with Python list :

Code 1 : Comparing memory

import numpy as np
import time 
import sys

# Creating a NumPy array with 100 elements
array = np.arange(100)
# array.itemsize : Size of one element
# array.size : length of array
print("Size of NumPy array : ", array.itemsize * array.itemsize)

# Creating a list with 100 elements
# Now I'll print the size of list
list = range(0, 10)
# Multiplying size of 1 element with length of the list
print("Size of list : ", sys.getsizeof(1)*len(list))

Output :

Size of NumPy array :  64
Size of list :  280

It clearly indicates that NumPy array consumes less memory as compared to the Python list

Code 2 : Fast Computation of NumPy array

import numpy as np
import time
import sys

# let's declare the size
Size = 100000

# Creating two lists
list1 = range(Size)
list2 = range(Size)

# Creating two NumPy arrays
arr1 = np.arange(Size)
arr2 = np.arange(Size)

# Calculating time for Python list
start = time.time()
result = [(x y) for x,y in zip(list1, list2)]
          
print("Time for Python List in msec : ", (time.time() - start) * 1000)

# Calculating time for NumPy array
start = time.time()
result = arr1   arr2
print("Time for NumPy array in msec : ", (time.time()- start) * 1000)

print("\nThis means NumPy array is faster than Pyhton List")

Output :

Time for Python List in msec :  11.117696762084961
Time for NumPy array in msec :  1.2216567993164062

This means NumPy array is faster than Pyhton List

Code 3 : NumPy is convenient

import numpy as np
import time

a1 = np.array([1, 2, 3])
a2 = np.array([4, 5, 6])

# To add two array you can simply do it by
print("ADD a1 and a2 elements : ", a1   a2)

# To sub two array you can simply do it by
print("SUB a1 and a2 elements : ", a1 - a2)

# To mul two array you can simply do it by
print("MUL a1 and a2 elements : ", a1 * a2)

# Calculating time for NumPy array
start = time.time()
result = arr1   arr2
print("Time for NumPy array in msec : ", (time.time()- start) * 1000)

Output :

ADD a1 and a2 elements :  [5 7 9]
SUB a1 and a2 elements :  [-3 -3 -3]
MUL a1 and a2 elements :  [ 4 10 18]

All the above 3 codes clearly validates the point that why we prefer NumPy array over Python list.