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·3 min read

Which is Faster? #include vs import

A deep dive into how C, Python, and Java handle external code — and why 'faster' depends on what you're measuring.

Which is Faster? #include vs import
💡The Question That Started It All

"Which one is faster #include in C or import in Python and Java?"

So a few days ago, my maths faculty casually asked me this question in class:

"Which one is faster #include in C or import in Python and Java?"

I did not have an immediate answer then.

And honestly, I had never really thought about it that way.

I use all of them regularly. I know how to write them. But I'd never stopped to ask what they're actually doing behind the scenes, or how that affects performance.

So, like any developer today, I did what we all do.

I GOOGLE-ed it.

And here's what I found.

Before We Compare: What Does "Faster" Actually Mean?

But before comparing anything, we need to clear up one thing.

"Faster" can mean two very different things:

1. Compile / import time — how quickly the program is prepared to run

2. Execution time — how fast the program runs after it starts

Most of the time, when people ask "which is faster?", they're really talking about execution speed, not how long it takes to compile or import.

So that's the angle we'll focus on.

How #include Works in C

In C, #include is handled by the preprocessor, even before compilation begins.

What actually happens is straightforward:

  • The contents of the header file are literally copied into your source file
  • The compiler then converts everything into native machine code
  • By the time the program runs, #include has already done its job

There is no runtime overhead related to #include.

ℹ️Performance Impact: C
  • Compilation: Can be slower
  • Execution: Extremely fast
  • Code runs: Directly on hardware

This is why C programs are known for their speed.

How import Works in Python

Python takes a completely different approach.

In Python, imports happen at runtime:

  • The module is loaded when the program starts running
  • Python is dynamically typed
  • Imported modules are cached after the first import

This design makes Python flexible and easy to work with, but it comes at a cost.

ℹ️Performance Impact: Python
  • Development speed: Very fast to write and experiment with code
  • Execution speed: Slower compared to C and Java
  • Runtime overhead: Imports add startup time

Python clearly prioritizes simplicity and developer productivity over raw performance.

How import Works in Java

Java sits somewhere between C and Python.

In Java:

  • No code is copied into your source file
  • Imports mainly help the compiler locate classes
  • Code runs inside the Java Virtual Machine (JVM)
  • The JVM uses Just-In-Time (JIT) compilation to optimize performance
ℹ️Performance Impact: Java
  • Startup: Slight overhead
  • Long-running programs: Benefit from runtime optimizations
  • Speed: Faster than Python, slightly slower than C

Java strikes a balance between performance, safety, and portability.

Speed Comparison Summary

LanguageExecution SpeedDev & Startup Speed
CFastest (native machine code)Slower compile times
JavaFast (JVM with JIT)Medium
PythonSlowest (interpreted / VM-based)Fastest to write and run

Final Verdict

So, what's the answer to that original question?

C is the fastest at runtime because it compiles directly to machine code Java is slightly slower, but JIT compilation makes it very efficient for large applications Python is the slowest in execution, but excels in productivity and ease of use

Have thoughts on this? Did I miss something? I'm still learning — reach out and tell me about your experience with these languages.

Happy coding! 🚀

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