[grog_tremor]

Eh, you massively overestimate the importance of performance.

For the vast majority of use cases, performance just isn't a priority. Doubly so for Python, that shines for simple automation, command line applications, and perhaps some serveless computing.

Being easy to write, having a good ecosystem of libraries, and being widely known is typically good enough. I wouldn't use Python to write a robust backend server side application, mostly because the language doesn't lend itself well for it.

[deafpolygon]

Eh, you make incorrect assumptions about me. I'm stating a fact why Python is used - the data science ecosystem in Python thrives because of well-written libraries _written in C_ under the hood AND an easy-to-use language that writes like pseudocode.

If it was too slow, we'd be doing all of this in Java, the C# or maybe doing it in C/Fortran. But because of some early design decisions (Guido being on the matrix-sig helped), the history behind Numeric/Numarray and finally NumPy and SciPy being based on those efforts allowed it to thrive.

[grog_tremor]

> it's the only way a tragically slow language like Python can keep up.

Those were your words, not mine. I need not make any assumptions.

I just replied listing use cases where Python shine due to its strengths, performance being mostly irrelevant. I didn't even mention data science.

And although it's beyond the point, if I was to use Python, why should I care in which language a library was written? If the language allows libraries written in other languages, this is actually a nice feature.

[UncleEntity]

> If the language allows libraries written in other languages, this is actually a nice feature.

That’s actually my primary use case for python, playing with C/C++ libraries in a repl because they don’t natively have one.

Sure, it takes work to wrap a library but that’s something I enjoy doing.