It might be hard to believe, but the Python programming language isn’t new and is actually more mature than the Java™ language and even HTTP. Unfortunately, however, one of the common misconceptions about Python that continues to persist is that Python is slow.
This misconception is rooted in the fact that interactive versions of Python that use an interpreter and standard Python, which uses a built-in compiler called CPython, are indeed slow. But, while Python interpreters and the Python language may be slower than Fortran or C, Python runtime code is not necessarily slow. Scientific computing packages such as SciPy and NumPy don’t have many of the shortcomings of standard Python.
Besides, there are other major Python implementations than standard Python. These implementations, known as distributions, may in fact be in more widespread use than the standard Python distribution. In addition, you can compile Python to accelerate runtime. Some compiler implementations, such as the Just-in-Time (JIT) compiler PyPy, can produce runtime code that can run as fast or faster than C.
In this post, let’s look at some of the compelling reasons to adopt Python for scientific research. Before we look at the merits of Python, let’s look at the tools researchers currently prefer for scientific research. In a follow-up article, Accelerating Python for scientific research, we’ll look at Python performance optimization and acceleration.