Which is faster list or array in python using

Elijah 261 Published: 06/19/2024

Which is faster list or array in python using

I'd be happy to help with that!

In Python, both lists and arrays are data structures used to store collections of items. The main difference between the two is how they are implemented.

A list is a dynamic sequence of objects (Python's built-in collection data type). It can grow or shrink dynamically as elements are added or removed. Lists are mutable, meaning their contents can be changed after creation. Lists are denoted by square brackets [].

An array, on the other hand, is a fixed-size, homogeneous collection of values. In Python, there isn't a built-in array data type like in some other languages (e.g., C, Java). Instead, Python uses NumPy arrays (part of the NumPy library) or arrays from third-party libraries like numpy or scipy.

When it comes to performance, lists are generally faster than NumPy arrays for small collections. This is because lists are implemented in CPython as a dynamic array (a contiguous block of memory), which has fewer overheads.

Here's a brief comparison:

Lists: Faster for small collections (<100 elements) Good for small to medium-sized datasets Easy to use and manipulate Dynamic: can grow or shrink dynamically NumPy arrays (or other array libraries): Slower for small collections (<100 elements) Better suited for large datasets (>10,000 elements) More memory-efficient for large datasets Faster for numerical operations

Here's a simple benchmark to illustrate the performance difference:

import timeit

def list_test(n):


lst = []


for i in range(n):


lst.append(i)


def numpy_array_test(n):


import numpy as np


arr = np.zeros((n,))


for i in range(n):


arr[i] = i

Small collection (100 elements)

small_n = 100


print(f"Testing with {small_n} elements...")


lst_time = timeit.timeit(lambda: list_test(small_n), number=1000)


numpy_arr_time = timeit.timeit(lambda: numpy_array_test(small_n), number=1000)


print(f"List: {lst_time:.2f}s")


print(f"NumPy array: {numpy_arr_time:.2f}s")

Large collection (10,000 elements)

large_n = 10000


print(f"nTesting with {large_n} elements...")


lst_time = timeit.timeit(lambda: list_test(large_n), number=1)  # single run for large n


numpy_arr_time = timeit.timeit(lambda: numpy_array_test(large_n), number=1)  # single run for large n


print(f"List: {lst_time:.2f}s")


print(f"NumPy array: {numpy_arr_time:.2f}s")

In this benchmark, the results show that lists are indeed faster for small collections (100 elements). However, as the collection size increases (10,000 elements), NumPy arrays become more efficient. This is because NumPy arrays use a contiguous block of memory, which has fewer overheads than Python's dynamic allocation for lists.

Keep in mind that these results are specific to this simple benchmark and may not reflect real-world performance differences. Always profile your code and consider the requirements of your project when choosing between lists or NumPy arrays.

Hope this helps clarify the performance difference between lists and arrays (NumPy) in Python!

Why is NumPy array better than Python list?

I'm happy to respond in English!

NumPy arrays are indeed more powerful and efficient than Python lists for several reasons. Here are some key differences:

Vectorized operations: NumPy arrays can perform element-wise operations on entire arrays at once, which is much faster than looping through a list in Python. This is particularly useful when working with large datasets or performing complex calculations. Memory efficiency: NumPy arrays store data more compactly than lists, reducing memory usage and making them better suited for handling massive datasets. Multidimensional support: NumPy arrays can easily handle multidimensional structures (e.g., matrices, tensors), which is essential in many scientific computing applications. Lists can only represent a single dimension of data. Vectorized indexing and slicing: NumPy arrays offer more flexible and efficient ways to index and slice arrays than lists do. This makes it easier to manipulate specific parts of the array or access specific elements. Integration with other libraries: NumPy arrays are well-suited for use with other scientific computing libraries, such as SciPy, Matplotlib, and Pandas. These libraries often rely on NumPy's capabilities to efficiently perform complex operations. Support for data types: NumPy arrays can store various data types (e.g., integers, floats, complex numbers) within a single array, whereas Python lists are limited to a specific type. Broadcasting: NumPy arrays can automatically adjust their shape and size based on the operation being performed. This "broadcasting" feature allows for more concise and readable code when working with arrays of different sizes. Support for advanced mathematical operations: NumPy provides an extensive range of mathematical functions, including support for linear algebra, Fourier transforms, and statistical analysis. Lists in Python don't offer these advanced capabilities out of the box. NumPy's broadcasting feature: As mentioned earlier, this allows for more flexibility when performing operations on arrays of different shapes.

In summary, NumPy arrays are better suited for large-scale numerical computations, data manipulation, and scientific computing applications due to their ability to handle multidimensional structures, support advanced mathematical operations, and perform vectorized operations. Python lists, while still useful for many tasks, are not as well-suited for these specific use cases.

Hope this answers your question!

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