DataCamp Light | Standalone example

This shows some performance numbers of operations between Python and Numpy. Notice how the 2nd set of numbers (NumPy) are always smaller - meaning they have much better performance than their Python List core library conterparts.


import time
import numpy as np

size_of_vec = 1000

def pure_python_version():
    t1 = time.time()
    X = range(size_of_vec)
    Y = range(size_of_vec)
    Z = [X[i] + Y[i] for i in range(len(X)) ]
    return time.time() - t1

def numpy_version():
    t1 = time.time()
    X = np.arange(size_of_vec)
    Y = np.arange(size_of_vec)
    Z = X + Y
    return time.time() - t1


t1 = pure_python_version()
t2 = numpy_version()
print(t1, t2)
print("Numpy is in this example " + str(t1/t2) + " faster!")

Just PressRun.

This shows some performance numbers of operations between Python and Numpy. Notice how the 2nd set of numbers (NumPy) are always smaller - meaning they have much better performance than their Python List core library conterparts.


import numpy as np
from timeit import Timer

size_of_vec = 1000
X_list = range(size_of_vec)
Y_list = range(size_of_vec)
X = np.arange(size_of_vec)
Y = np.arange(size_of_vec)

def pure_python_version():
    Z = [X_list[i] + Y_list[i] for i in range(len(X_list)) ]

def numpy_version():
    Z = X + Y

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

print(timer_obj1.timeit(10))
print(timer_obj2.timeit(10))  # Runs Faster!

print(timer_obj1.repeat(repeat=3, number=10))
print(timer_obj2.repeat(repeat=3, number=10)) # repeat to prove it!

Just PressRun.