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I mean, it's obviously unfair. Even in the scientific community someone uses PyPy. As far as download statistics go, I think between 0.5-1% of downloads from Py

by fijal 10y ago

I mean, it's obviously unfair. Even in the scientific community someone uses PyPy. As far as download statistics go, I think between 0.5-1% of downloads from PyPI are for PyPy. This is far from "majority" but far from "noone" as well. For what is worth enough big players use PyPy that the consulting company that I run can stay afloat, so I somehow doubt that's the case :-)

nbadg 10y ago
From an outside perspective with no skin in the game (other than using Python profusely and just generally wanting it to be the "best" possible Python it can be) I wouldn't be so quick to call it obviously unfair. The exact phrasing was: > PyPy is ten years old at this point, but to a first approximation, no one is using it. Using your 0.5% - 1.0% PyPI metric as an approximation for how many people are actually using pypy, I think it's reasonable to say that. I personally would definitely have phrased it differently, but if >=99% of the market isn't using PyPy, then it is far, far, far removed from the "mainstream" market. In fairness, the Python community is, on the whole, large enough to give you a reasonably sustainable niche, but it pales in comparison to the market itself. It's a bit like comparing Facebook to CouchSurfing, to be honest. You know about it, maybe even have some friends who have done it, and hey, it's still 3 million people, but in comparison to Facebook... no one uses it. And that's not a value judgment or anything, it's just a scale of comparison kind of thing. That being said, I share your frustration about the deep schism between web vs scientific in the Python community, and I think it's really a shame for the community as a whole. Unfortunately I'm not sure that's going to change very soon: the web side of things has a tremendous amount of momentum (and money) invested in "their" architecture, and at the same time, the scientific side of things has far, far less patience for pain points in their language tooling. Put differently, if your background and job is programming, you're more likely to view "dealing with this programming problem" as actual work, but if your background and job is "I have this data, and I need to analyze it", then "fiddling with this programming problem" is, at best, a frustrating distraction from your actual task. And I think both arenas need to have a better appreciation for the other: as programmers, our tools generally really do suck for everyone, we're just used to it; as data scientists, we are woefully under-aware of how difficult these programming problems can be. Some more unity would be tremendously beneficial for all. And then on top of it all, there's this whole group of weirdos using Python for stuff like desktop applications (I happen to be in this camp). Good luck finding a packaging and deployment solution there!

by fijal 10y ago

nbadg 10y ago

From an outside perspective with no skin in the game (other than using Python profusely and just generally wanting it to be the "best" possible Python it can be) I wouldn't be so quick to call it obviously unfair. The exact phrasing was: > PyPy is ten years old at this point, but to a first approximation, no one is using it. Using your 0.5% - 1.0% PyPI metric as an approximation for how many people are actually using pypy, I think it's reasonable to say that. I personally would definitely have phrased it differently, but if >=99% of the market isn't using PyPy, then it is far, far, far removed from the "mainstream" market. In fairness, the Python community is, on the whole, large enough to give you a reasonably sustainable niche, but it pales in comparison to the market itself. It's a bit like comparing Facebook to CouchSurfing, to be honest. You know about it, maybe even have some friends who have done it, and hey, it's still 3 million people, but in comparison to Facebook... no one uses it. And that's not a value judgment or anything, it's just a scale of comparison kind of thing. That being said, I share your frustration about the deep schism between web vs scientific in the Python community, and I think it's really a shame for the community as a whole. Unfortunately I'm not sure that's going to change very soon: the web side of things has a tremendous amount of momentum (and money) invested in "their" architecture, and at the same time, the scientific side of things has far, far less patience for pain points in their language tooling. Put differently, if your background and job is programming, you're more likely to view "dealing with this programming problem" as actual work, but if your background and job is "I have this data, and I need to analyze it", then "fiddling with this programming problem" is, at best, a frustrating distraction from your actual task. And I think both arenas need to have a better appreciation for the other: as programmers, our tools generally really do suck for everyone, we're just used to it; as data scientists, we are woefully under-aware of how difficult these programming problems can be. Some more unity would be tremendously beneficial for all. And then on top of it all, there's this whole group of weirdos using Python for stuff like desktop applications (I happen to be in this camp). Good luck finding a packaging and deployment solution there!