1.3 Okay, But What's the Downside?

Perhaps the only downside to Python is that, as currently implemented, its execution speed may not always be as fast as compiled languages such as C and C++.

We'll talk about implementation concepts later in this book. But in short, the standard implementations of Python today compile (i.e., translate) source code statements to an intermediate format known as byte code, and then interpret the byte code. Byte code provides portability, as it is a platform-independent format. However, because Python is not compiled all the way down to binary machine code (e.g., instructions for an Intel chip), some programs will run more slowly in Python than in a fully compiled language like C.

Whether you will ever care about the execution speed difference depends on what kinds of programs you write. Python has been optimized numerous times, and Python code runs fast enough by itself in most application domains. Furthermore, whenever you do something "real" in a Python script, like process a file or construct a GUI, your program is actually running at C speed since such tasks are immediately dispatched to compiled C code inside the Python interpreter. More fundamentally, Python's speed-of-development gain is often far more important than any speed-of-execution loss, especially given modern computer speeds.

Even at today's CPU speeds there still are some domains that do require optimal execution speed. Numeric programming and animation, for example, often need at least their core number-crunching components to run at C speed (or better). If you work in such a domain, you can still use Python—simply split off the parts of the application that require optimal speed into compiled extensions , and link those into your system for use in Python scripts.

We won't talk about extensions much in this text, but this is really just an instance of the Python-as-control-language role that we discussed earlier. A prime example of this dual language strategy is the NumPy numeric programming extension for Python; by combining compiled and optimized numeric extension libraries with the Python language, NumPy turns Python into a numeric programming tool that is both efficient and easy to use. You may never need to code such extensions in your own Python work, but they provide a powerful optimization mechanism if you ever do.