NumPy array vs Python list performance

Introduction

We want to know which is faster: a NumPy array or a Python list. This helps us choose the best tool for working with numbers.

When you need to do math on many numbers quickly.

When you want to store a large collection of numbers efficiently.

When you want to compare how fast different data structures work.

When you want to learn about performance in data science tasks.

Syntax

NumPy

import numpy as np
import time

# Create a Python list
python_list = [i for i in range(1000000)]

# Create a NumPy array
numpy_array = np.array(python_list)

# Measure time for sum using Python list
def sum_python_list(numbers):
    total = 0
    for number in numbers:
        total += number
    return total

# Measure time for sum using NumPy array
def sum_numpy_array(numbers):
    return np.sum(numbers)

NumPy arrays are made for fast math operations on numbers.

Python lists can hold any type but are slower for math on many numbers.

Examples

Both can be empty. Summing empty collections returns 0.

NumPy

python_list = []  # empty list
numpy_array = np.array([])  # empty NumPy array

Both can hold one number. Summing returns that number.

NumPy

python_list = [42]  # one element list
numpy_array = np.array([42])  # one element NumPy array

Both hold multiple numbers. NumPy sum is usually faster.

NumPy

python_list = [1, 2, 3, 4, 5]  # list with multiple elements
numpy_array = np.array([1, 2, 3, 4, 5])  # NumPy array with same elements

Sample Program

This program creates a big list and a NumPy array with the same numbers. It sums them both and shows how long each takes. You will see NumPy is faster.

NumPy

import numpy as np
import time

# Create a Python list with 1 million numbers
python_list = [i for i in range(1000000)]

# Create a NumPy array from the same numbers
numpy_array = np.array(python_list)

# Function to sum numbers in Python list

def sum_python_list(numbers):
    total = 0
    for number in numbers:
        total += number
    return total

# Function to sum numbers in NumPy array
def sum_numpy_array(numbers):
    return np.sum(numbers)

# Measure time for Python list sum
start_time_list = time.time()
sum_list = sum_python_list(python_list)
end_time_list = time.time()

# Measure time for NumPy array sum
start_time_numpy = time.time()
sum_numpy = sum_numpy_array(numpy_array)
end_time_numpy = time.time()

# Print results
print(f"Sum using Python list: {sum_list}")
print(f"Time taken by Python list sum: {end_time_list - start_time_list:.6f} seconds")
print(f"Sum using NumPy array: {sum_numpy}")
print(f"Time taken by NumPy array sum: {end_time_numpy - start_time_numpy:.6f} seconds")

Important Notes

NumPy arrays use less memory and run math operations faster than Python lists.

Python lists are flexible but slower for large number crunching.

Use NumPy arrays when working with big numeric data for better speed.

Summary

NumPy arrays are faster than Python lists for math on many numbers.

Python lists are good for mixed data but slower for numeric tasks.

Choose NumPy arrays to improve performance in data science projects.