[xapata]

Python has made some trade-offs that you dislike. You complain about the negative consequences without comparing those against the benefits.

One of the major factors in speed is efficient memory layout. Contrast a Python list with a NumPy array. To achieve speedier loops and vectorized arithmetic [0], the array gives up dynamic typing and dynamic sizing. In most applications, I would gladly give up some compute speed to gain some programming productivity.

I love duck-typing. Formal typing has some impressive examples, but in the projects I've worked on has reduced my productivity. Perhaps because data are so often serialized to simple formats or written to databases that discard the best tools of formal typing. Anecdotal evidence, for sure.

I've never been bothered by the GIL, but I have benefitted from it [1].

[0] https://docs.continuum.io/mkl-optimizations/

[1] https://www.youtube.com/watch?v=P3AyI_u66Bw

[darkf]

>Contrast a Python list with a NumPy array. To achieve speedier loops and vectorized arithmetic [0], the array gives up dynamic typing and dynamic sizing. In most applications, I would gladly give up some compute speed to gain some programming productivity.

Except numpy arrays have a much richer interface and can still store dynamic objects (dtype=object). So what's your point?

>I love duck-typing

So do I. Where does this come from? I don't believe I ever considered it a contra.

[nneonneo]

Have you ever tried appending to a NumPy array in a loop? It's a total disaster! And dtype=object arrays are mostly useless; they gain almost none of the benefits of regular NumPy (you may as well run np functions on plain lists) and play poorly with other types. NumPy is great for numerics and structured data - lists are general purpose structures for data manipulation. They are different, have different goals and trade offs, and I don't think it's appropriate to claim that one size should fit all.

[xapata]

If you're storing generic objects rather than numbers in a NumPy array, you're discarding its main benefits. Sure, you've got some extra slicing sugar for selecting columns and subsets, but comprehensions are more readable (and faster!) in many of those situations.

Duck-typing is Python's form of dynamic typing and therefore results in the speed penalty. If you want the extra speed, you'll need to give up some dynamicism. I say this now, but some of the work the core devs are doing to optimize dicts might let us have our cake and eat it too. Until then, it's a choice: flexible or fast, not both.

[stcredzero]

One of the major factors in speed is efficient memory layout.

In most applications, I would gladly give up some compute speed to gain some programming productivity.

By Smalltalk standards, Python is pretty profligate. (By 90's C programmer standards, Smalltalk is pretty profligate.) However, Smalltalk still has many of the high productivity features as Python. (In fact, the debugging story is far superior.) I suspect, though, that Python is still a far superior environment for the things you use it for.

[xapata]

I enjoyed coding homework assignments in Smalltalk, but for some reason I never tried using it professionally.