Comparing lists and NumPy arrays - 1.py

import random
import sys
import time

import numpy as np

numbers = [random.randint(-100, 450) / 10 for _ in range(10_000_000)]
numbers_np = np.array(numbers)

def convert_list(data):
    output = []
    for value in data:
        output.append(value * 1.8 + 32)
    return output

def convert_list_comp(data):
    return [value * 1.8 + 32 for value in data]

def convert_numpy(data: np.ndarray):
    return data * 1.8 + 32


print(f"Using Python version {sys.version}")

# Confirm all versions give the same result
print(
    convert_list(numbers)
    == convert_list_comp(numbers)
    == list(convert_numpy(numbers_np))
)

# Run all three functions and time them
start = time.time()
convert_list(numbers)
time_list = time.time() - start

start = time.time()
convert_list_comp(numbers)
time_comp = time.time() - start

start = time.time()
convert_numpy(numbers_np)
time_numpy = time.time() - start

# Show results
print(f"\nUsing list: {time_list:0.5f} s")
print(f"Using list comprehension: {time_comp:0.5f} s")
print(f"Using numpy: {time_numpy:0.5f} s")

print(
    f"\nList comp is {time_list/time_comp:0.2f} times faster than lists"
)
print(
    f"Numpy array is {time_list/time_numpy:0.2f} times faster than lists"
)

# OUTPUT:
# Using Python version 3.10.5 (v3.10.5:f377153967, Jun  6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]
# True
#
# Using list: 0.59864 s
# Using list comprehension: 0.36916 s
# Using numpy: 0.01285 s
#
# List comp is 1.62 times faster than lists
# Numpy array is 46.59 times faster than lists