NumPy: Test whether numpy array is faster than Python list or not

Last update on April 26 2025 12:40:27 (UTC/GMT +8 hours)

Compare speed of NumPy array vs Python list.

Write a Numpy program to test whether numpy array is faster than Python list or not.

Sample Solution:

Python Code:

 # Importing necessary libraries
import time  # Importing time module for time-related functionalities
import numpy as np  # Importing NumPy library

# Defining the size of the arrays
SIZE = 200000

# Creating lists and NumPy arrays of integers from 0 to SIZE - 1
list1 = range(SIZE)
list2 = range(SIZE)
arra1 = np.arange(SIZE)
arra2 = np.arange(SIZE)

# Measuring the time taken to aggregate elements from each iterable using a list comprehension
start_list = time.time()  # Marking the start time
result = [(x, y) for x, y in zip(list1, list2)]  # Aggregating elements from 'list1' and 'list2' using list comprehension
print("Time to aggregate elements from each of the iterables:")
print("List:")
print((time.time() - start_list) * 1000)  # Printing the execution time in milliseconds

# Measuring the time taken to add NumPy arrays element-wise
start_array = time.time()  # Marking the start time
result = arra1 + arra2  # Performing element-wise addition on 'arra1' and 'arra2' using NumPy
print("NumPy array:")
print((time.time() - start_array) * 1000)  # Printing the execution time in milliseconds

Sample Output:

Time to aggregates elements from each of the iterables:
List:
72.64399528503418
NumPy array:
19.61684226989746

Explanation:

In the above code -

• SIZE: A constant integer variable with the value of 200000, representing the size of the lists and arrays.
• list1 and list2: Two Python lists created using the range function, each containing integers from 0 to SIZE-1.
• arra1 and arra2: Two NumPy arrays created using the np.arange function, each containing integers from 0 to SIZE-1.
• start_list: Records the current time before the list element-wise addition starts.
• result: A list comprehension with zip is used to add list1 and list2 element-wise. It creates a list of tuples containing the sum of corresponding elements from list1 and list2.
• print((time.time()-start_list)*1000): This line calculates the time taken for the list addition operation in milliseconds and prints it.
• start_array: Records the current time before the NumPy array element-wise addition starts.
• result = arra1 + arra2: This line adds the two NumPy arrays arra1 and arra2 element-wise directly.
• print((time.time()-start_array)*1000): Calculates the time taken for the NumPy array addition operation in milliseconds and prints it.

For more Practice: Solve these Related Problems:

• Write a NumPy program to perform a large-scale arithmetic operation on both a NumPy array and a Python list and measure the execution time using timeit.
• Create a function that aggregates elements from a list and a NumPy array and compares the performance using %timeit in Jupyter.
• Implement a solution that benchmarks element-wise multiplication for both iterables and reports the speedup factor.
• Test the performance comparison with arrays/lists of increasing sizes to observe scalability differences.

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• NumPy Array Exercises Home ↩
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