Programming Python, 3rd Edition

What’s Python Not Good For?

To be fair again, some tasks are outside of Python’s scope. Like all dynamic interpreted languages, Python, as currently implemented, isn’t generally as fast or efficient as static, compiled languages such as C (see the earlier sidebar, "How Python Runs Your Code,” for the technical story). At least when nontypical benchmarks are compared line for line, Python code runs more slowly than C code.

Whether you will ever care about this difference in execution speed depends upon the sorts of applications you will write. In many domains, the difference doesn’t matter at all; for programs that spend most of their time interacting with users or transferring data over networks, Python is usually more than adequate to meet the performance needs of the entire application by itself.

Moreover, most realistic Python programs tend to run very near the speed of the C language anyhow. Because system interactions such as accessing files or creating GUIs are implemented by linked-in C language code in the standard implementation, typical Python programs are often nearly as fast as equivalent C language programs. In fact, because Python programs use highly optimized data structures and libraries, they are sometimes quicker than C programs that must implement such tools manually.

In some domains, however, efficiency is still a main priority. Programs that spend most of their time in intense number crunching, for example, will usually be slower in Python than in fully compiled ...