Python效率咨询：a=array[x][y]与直接调用array[x][y]及函数封装的效率对比

Great questions about optimizing Python code—let’s break them down with practical context and examples.

1. a = array[x][y] vs Directly Using array[x][y] #

The answer depends on how many times you need to access that value:

• If you use array[x][y] only once: There’s almost no difference in efficiency. Assigning to a variable adds a tiny overhead of storing the reference, but it’s negligible for single-use cases. Readability might be the bigger factor here—if the index is complex, assigning to a named variable makes your code clearer.

• If you use array[x][y] multiple times (e.g., in a loop): Assigning to a variable is significantly more efficient. Here’s why: every time you write array[x][y], Python has to perform two lookups: first it fetches the sub-array at array[x], then it fetches the element at index y of that sub-array. By storing the result in a variable, you do those two lookups once, then reuse the value directly.

Let’s test this with timeit to see the difference:

import timeit

# Setup: Create a 1000x1000 2D list
setup_code = """
arr = [[i for i in range(1000)] for _ in range(1000)]
"""

# Directly accessing the index 1000 times
direct_stmt = """
total = 0
for _ in range(1000):
    total += arr[500][500]
"""

# Assign to variable first, then reuse
var_stmt = """
total = 0
val = arr[500][500]
for _ in range(1000):
    total += val
"""

# Run the tests
print("Direct access time:", timeit.timeit(direct_stmt, setup=setup_code, number=1000))
print("Variable assignment time:", timeit.timeit(var_stmt, setup=setup_code, number=1000))

You’ll see the variable assignment version runs much faster—often 2-3x quicker for this kind of repeated access.

2. Wrapping Operations in a Single Function: Does It Boost Efficiency? #

Yes, most of the time! Here’s the key reason: local variable lookups in Python are far faster than global variable lookups.

When you run code in the global scope, every variable reference has to search through the global namespace (a dictionary-like structure). Inside a function, variables are stored in a local array, and lookups use direct indexing—this is way quicker.

The tradeoff is that function calls have a small overhead, but for any non-trivial operation, the speed gain from local variable lookups easily outweighs that overhead.

Let’s compare global vs. function-based code:

import timeit

# Global scope operation
global_data = [i for i in range(10000)]
def global_calculation():
    total = 0
    for num in global_data:
        total += num
    return total

# Function-scoped operation (all variables local)
def local_calculation():
    data = [i for i in range(10000)]
    total = 0
    for num in data:
        total += num
    return total

print("Global scope time:", timeit.timeit(global_calculation, number=1000))
print("Function scope time:", timeit.timeit(local_calculation, number=1000))

The function-based version will almost always run faster here because data and total are local variables. Even if you’re using global variables inside a function, you can optimize by assigning them to local variables first (e.g., local_data = global_data) to speed up lookups.

One caveat: For extremely simple operations (like a single arithmetic calculation), the function call overhead might make it slightly slower. But in real-world code, where you’re doing multiple steps, wrapping logic in functions is both more efficient and better for code organization.

内容的提问来源于stack exchange，提问作者Victor. L