Compare Python List & Numpy Array

• Create two lists:

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# Create two python List say 'List_x' and 'List_your'
X_list = list(range(100000))
Y_list = list(range(100000))

• Import NumPy and Create two numpy arrays using the two Python lists:

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# Import numpy Library
import numpy as np
# Create two numpy array (say 'X' end 'Y') using above two list
X = np.array(X_list)
Y = np.array(Y_list)

• Define a function that will perform element-wise addition of two Python lists.

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# Define a function to calculate sum
# using python Lists X_list, Y_list)
def pure_python_version():
    Z = []
    for i in range(len(X_list)):
        Z.append(X_list[i] + Y_list[i])

• Define a function that will perform element-wise addition of two NumPy arrays.

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# Define a function to calculate sum
# using numpy arrays (X, Y)
def numpy_version():
# In numpy we can directly perform
# sum element-wise as
    Z = X + Y 

• Notice the time taken by both the functions (i.e. list_sum() and numpy_arr_sum()) to perform the same task.

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# Import Timer class from timeit Library
from timeit import Timer

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timer_obj1 = Timer("pure_python_version()", 
                   "from __main__ import pure_python_version")
timer_obj2 = Timer("numpy_version()", 
                   "from __main__ import numpy_version")

print("Pure python version:",timer_obj1.timeit(1000))
print("Numpy version:",timer_obj2.timeit(1000))

Output:

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Pure python version: 18.138080474000162
Numpy version: 0.16344507999929192

• You can observe here that the time taken by the python list to do the element-wise sum is much more than the NumPy arrays.