Why is [] Faster Than list() in Python?

When optimizing Python code, you may encounter situations where the difference between two seemingly identical operations can significantly impact performance. One such case is the difference in speed between using [] to create an empty list and using list(). Although both methods produce the same result—an empty list—the former is notably faster. Let’s dive into why this happens and how it might affect your Python code.

Understanding Literal Syntax vs. Function Calls

In Python, [] is a literal syntax for creating lists, while list() is a built-in function that generates a new list. The performance difference stems from how Python handles these two operations under the hood.

When you use [], Python is directly creating a new list object. The operation is straightforward and handled at the bytecode level by the BUILD_LIST instruction. This instruction is simple and optimized for speed, as it doesn’t require any function calls or additional overhead.

On the other hand, list() is a function call that involves several more steps:

1. Name Lookup: Python must first resolve the name list by searching through the local, global, and built-in namespaces.
2. Function Call Overhead: Once the list name is resolved, Python invokes the list() function, which includes managing the call stack, passing arguments (even if there are none), and handling the return value.

This additional work makes list() slower compared to using [] directly.

Example: Timing the Difference

To see this difference in action, consider the following examples using Python’s timeit module:

import timeit

# Timing [] for creating an empty list
time_for_brackets = timeit.timeit('[]', number=10000000)
print(f"Time for []: {time_for_brackets} seconds")

# Timing list() for creating an empty list
time_for_list_func = timeit.timeit('list()', number=10000000)
print(f"Time for list(): {time_for_list_func} seconds")

On a typical machine, you might see output similar to this:

Time for []: 0.254 seconds
Time for list(): 0.484 seconds

This example clearly shows that [] is nearly twice as fast as list() for creating an empty list.

Why This Matters: Performance in Real Applications

While the difference in speed might seem trivial for a single operation, it can accumulate in large-scale applications or performance-critical sections of code. For example, if you’re initializing a large number of empty lists in a loop, using [] instead of list() can lead to noticeable performance improvements.

Consider the following loop:

# Using [] in a loop
def create_lists_with_brackets():
    for _ in range(1000000):
        _ = []

# Using list() in a loop
def create_lists_with_list_func():
    for _ in range(1000000):
        _ = list()

# Timing both functions
time_for_brackets_loop = timeit.timeit(create_lists_with_brackets, number=1)
time_for_list_func_loop = timeit.timeit(create_lists_with_list_func, number=1)

print(f"Time for [] loop: {time_for_brackets_loop} seconds")
print(f"Time for list() loop: {time_for_list_func_loop} seconds")

Running this code may yield results where the [] loop is consistently faster than the list() loop.

Why the Difference Exists

The root of the performance difference lies in Python’s need to be flexible. The list function can be overridden or shadowed by user-defined functions or variables, which requires Python to perform additional checks and handle the function call dynamically. This flexibility, while powerful, introduces overhead that isn’t present when using the literal [] syntax.

For instance:

# Overriding the list function
def list():
    return "This is not a list"

# Testing the impact
print(list())  # Output: This is not a list
print([])      # Output: []

Here, list() no longer behaves as expected because it’s been overridden, but [] remains unaffected because it’s directly tied to Python’s internal mechanism for creating lists.

In summary, [] is faster than list() because it is a literal syntax directly translated into optimized bytecode, while list() involves additional overhead due to its nature as a function. Understanding these subtle differences can help you write more efficient Python code, especially in performance-critical sections. Whenever possible, prefer [] over list() for creating empty lists to gain that extra bit of speed.