Python Programming

Efficient List and Array Pre-allocation in Python

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Python lists dynamically resize, but pre-allocation can boost performance, especially with large datasets. This article explores efficient pre-allocation techniques for lists and other sequential data structures.

Table of Contents

Pre-allocating Python Lists

While Python doesn’t directly support pre-sized lists like some other languages, we can efficiently create them using list comprehensions or the * operator.

Method 1: List Comprehension

Ideal for creating lists of a specific size filled with a single repeating value:


size = 10
my_list = [0] * size  # List of 10 zeros
print(my_list)  # Output: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

my_list = [None] * size # List of 10 None values
print(my_list) # Output: [None, None, None, None, None, None, None, None, None, None]

Method 2: Using list() with a Generator

Provides flexibility for more complex initialization where each element requires a unique value:


size = 5
my_list = list(range(size))  # Creates [0, 1, 2, 3, 4]
print(my_list)

my_list = list(i**2 for i in range(size))  # Creates [0, 1, 4, 9, 16]
print(my_list)

Important Note: Pre-allocation primarily optimizes initial population by minimizing resizing. Appending beyond the initial size still triggers dynamic resizing.

Pre-allocating NumPy Arrays

NumPy arrays excel with numerical computation and large datasets. They allow direct size and data type specification:


import numpy as np

size = 10
my_array = np.zeros(size, dtype=int)  # Array of 10 zeros (integers)
print(my_array)

my_array = np.empty(size, dtype=float)  # Array of 10 uninitialized floats (use cautiously!)
print(my_array)

my_array = np.arange(size)  # Array [0, 1, 2, ..., 9]
print(my_array)

NumPy offers various functions for creating arrays with different initial values and data types, significantly enhancing numerical operation efficiency.

Pre-allocating with array.array

The array.array module provides compact storage for homogeneous data, requiring data type specification:


import array

size = 5
my_array = array.array('i', [0] * size)  # Array of 5 integers initialized to 0
print(my_array)

'i' specifies signed integer type; refer to the documentation for other type codes.

Choosing the Right Data Structure

The best choice (list, NumPy array, array.array) depends on your application and data. NumPy arrays are generally preferred for numerical computation due to performance advantages. For simple, homogeneous data, array.array might be more efficient than lists. Python lists remain versatile for general-purpose use with mixed data types, despite their dynamic resizing.

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