Let's break down your questions one by one—this is a common topic I've dug into while optimizing Python app startup times and distributing code.

Runtime Differences #

First things first: once loaded into the Python interpreter, there’s no difference in how the code executes. Both .py and .pyc files end up feeding the exact same bytecode to the Python virtual machine (VM).

The only real distinction is in the loading phase:

• When you run a .py file, the interpreter first compiles it to bytecode (and saves that as a .pyc in the __pycache__ directory if permitted), then executes the bytecode.
• When you use a .pyc file directly (or the interpreter finds a matching .pyc for an imported module), it skips the compilation step and loads the pre-compiled bytecode straight away.

The only "runtime" gotcha here is version compatibility: if a .pyc was compiled with a different Python major/minor version (e.g., 3.9 vs 3.10), the interpreter will refuse to load it. It’ll either look for the corresponding .py file to recompile, or throw an error if no .py exists.

Benchmark Tests #

Benchmarks mostly highlight differences in startup/load time, not execution speed. Here’s what you’ll typically find:

• Module import speed: Importing from .pyc is faster than .py because you skip the on-the-fly compilation step. For small modules, the difference is trivial, but for large codebases with dozens/hundreds of modules, this can cut down application startup time noticeably.
• Execution performance: Once the bytecode is loaded, runtime speed is identical across .py and .pyc—the VM runs the exact same instructions either way.

You can test this yourself with a quick timeit snippet:

import timeit

# First run: delete __pycache__ to force compiling from .py
print("Import from .py:", timeit.timeit("import my_large_module", number=100))

# Second run: uses the generated .pyc
print("Import from .pyc:", timeit.timeit("import my_large_module", number=100))

You’ll consistently see the second run (using .pyc) finish faster.

Reliability of Using .pyc Instead of .py #

Using .pyc as a replacement for .py is feasible, but comes with key caveats:

• Version lock-in: .pyc files are tied to the exact Python major/minor version they were compiled with. If your users run a different version, the interpreter will reject the .pyc and fail if no .py is available.
• Maintenance overhead: Every code update requires recompiling affected .py files to generate fresh .pycs. Forgetting this can lead to running outdated bytecode accidentally.
• Limited source protection: While .pyc isn’t human-readable like .py, tools like uncompyle6 can easily decompile it back to roughly equivalent Python code. Don’t rely on .pyc for secure source hiding.
• Cross-platform edge cases: Python bytecode is mostly cross-platform, but modules with platform-specific extensions might behave unexpectedly if their .pyc is moved between OSes (though this is rare).

That said, .pyc is totally reliable in controlled environments—like deploying to servers with a fixed Python version. Pre-compiling code to .pyc here speeds up startup without any downsides.

内容的提问来源于stack exchange，提问作者Benyamin Jafari