Why Python Code Runs Faster in a Function

In Python, you may notice that code runs faster when it is placed inside a function rather than running directly in the top-level script (global scope). This is due to how Python manages variable lookups and optimizes execution inside functions.

Functions have their own local scope, which is much faster to access compared to the global scope. Also, Python internally applies several optimizations when it detects function boundaries.

Reasons for Faster Execution in Functions

Here are the main reasons why Python code executes faster inside a function -

• Local Variable Access: Local variables are stored in a fixed-size array, which allows faster access than global variables that require dictionary lookups.
• Reduced Overhead: Python applies certain optimizations, like bytecode tuning, when it sees function blocks.
• Better Cache Usage: When code is inside a function, CPU caching is more effective due to smaller scope and repeated access patterns.
• Efficient Namespace: The function's local namespace is more predictable and less dynamic than the global namespace, making lookups faster.

Example: Loop Inside and Outside Function

Let us compare the performance of running a loop in the global scope vs inside a function. We use the time module to measure execution time in both cases -

import time

# Code outside function (global scope)
start = time.time()
for i in range(1000000):
   x = i * 2
end = time.time()
print("Global Scope:", end - start)

# Code inside function
def run_loop():
   for i in range(1000000):
      x = i * 2

start = time.time()
run_loop()
end = time.time()
print("Function Scope:", end - start)

We get output like this (timing may vary slightly depending on your system) -

Global Scope: 0.03804826736450195
Function Scope: 0.01887822151184082

As you can see, the code inside the function runs faster than the same logic outside. This happens because local variable access is faster inside functions due to optimized memory handling.

Variable Lookup: Local vs Global

In Python, variable access time depends on where the variable is stored. Global variables are stored in a dictionary, while local variables are stored in a fixed-size array.

Accessing local variables is faster because it is a direct access using index positions. On the other hand, global access needs hashing and dictionary lookups.

Example

In this example, Python uses faster access for y because it is in the local scope. Even though both x and y are numbers, y is accessed more efficiently -

x = 100  # Global variable

def compute():
   y = 100  # Local variable
   return y * 2

print(compute())

We get the output as shown below -

200

Function Optimization by Python Interpreter

In Python, code that is placed inside functions runs more efficiently than code at the global level. This is because the Python interpreter applies certain internal optimizations when working with functions. Some of these are -

• Faster variable access - When you use global variables inside a function, Python avoids repeatedly checking the global scope, improving performance.
• Bytecode generation - Python compiles function blocks into optimized bytecode, which runs faster compared to code written directly in the global scope.

Because of these optimizations, placing your code inside functions not only keeps it organized but can also lead to slightly faster execution.

Cleaner Memory Management

When you use functions in Python, they create their own local scope. Any variables defined inside the function exist only while the function is running. Once the function finishes, those variables are automatically cleared from memory.

This makes memory usage more efficient, especially in cases like loops or temporary calculations where variables are no longer needed afterward. On the other hand, variables defined at the global level stay in memory for the entire runtime of the program, even if they are no longer used. This can slightly reduce performance and increase memory usage over time.

Using functions not only helps organize your code but also leads to better memory handling and cleaner program execution.

Conclusion

Python code runs faster inside a function because of -

• Faster access to local variables
• Interpreter-level optimizations
• Better memory and namespace management
• Reduced lookup overhead

For better performance, especially in large or repetitive tasks, it is recommended to place your logic inside functions rather than executing it directly in the global scope.