I am working on a project where I need to pre-allocate an empty list of a specific size, like 100 or 1000 elements, and fill it with a placeholder like None or zero. I want to avoid using the append() method in a loop because I’m worried about the performance overhead as the list grows. Is there a built-in Pythonic way to create this fixed-size list immediately so I can just assign values to specific indices later?
3 answers
The most common and fastest way to initialize a list with a fixed size in Python is using the list multiplication operator. For example, to create a list of size 100 initialized with zeros, you would use my_list = [0] * 100. This method is highly efficient because it allocates the memory in a single step rather than dynamically resizing the list as you would with append(). However, be careful when using this with mutable objects like nested lists (e.g., [[]] * 10), because it creates multiple references to the exact same object in memory rather than distinct copies.
I've seen the multiplication method before, but how does it behave if I actually need a list of empty dictionaries or other objects that shouldn't share the same memory address? If I use the [ {} ] * 5 syntax, won't changing one dictionary affect all of them in the list? I need to ensure each element is independent.
If you are doing this for numerical data, you should skip lists entirely and use NumPy. import numpy as np; arr = np.zeros(100) is much faster for math-heavy tasks.
I agree with Nancy. In the data science domain, NumPy arrays are far superior to standard Python lists for fixed-size allocation because they use contiguous memory blocks, which significantly speeds up computation.
Christopher, you are absolutely right to be cautious. For mutable objects, you should use a list comprehension instead: my_list = [{} for _ in range(100)]. This ensures that a new, distinct dictionary is instantiated for every index in the list. While slightly slower than the multiplication operator, it is the standard "best practice" in software development for avoiding those nasty shared-reference bugs that are incredibly hard to debug in larger applications.