How to pronounce“numpy”in English

numpy because i just spent a week getting my python environment and if you change if you change numpy i have to reinstall

write numpy sci-fi right maybe but that's that's that's what i'm talking about when i say we're maybe at a post scarcity point for a lot

writing numpy and numpy was the python exposure i didn't really care about if you didn't have numpy installed anyway guido

with the numpy and vectorization capabilities i love thinking in terms of the matrices and the vectors and these kind of

to write numpy for others it's uh there is a saying in the in the sci-fi uh community that you know sci-fi advance is one failed

want to install numpy and pandas i want this version of some like geospatial library i want this other thing like why is this hard these exist right

somebody like uh like numpy or some major package to step into the cold well it's like it's a chicken and egg

he created numpy scipy and anaconda numpai formed the foundation of tensor-based machine learning in python

created scipy and numpy because it brought so much joy to my life i appreciate that i mean the tip dark notion was there i appreciate that and i

american number a and numpy where numpy created an array of records west started to use that almost like a data frame except it's an array of

up so numpy ultimately took a little while about six months in people started to join me you know francesc alted robert kern charles harris

so ultimately numpy became documentation driven development because i basically wrote the book and made sure the stuff worked to the book would work

supporting scipy and numpy itself wasn't as possible like they didn't they tried i mean it was not just because it was just because the business aspect so and

stuff about numpy or both sort of in a personal level but also like looking forward what kind of things would you like to see changed

really users of numpy so much it's the development of numpy so you're thinking about like how to design numpy so that it's

didn't get numpy into python but we got the basic structure of it in the python like so you could build on it nobody did for a while but eventually database

better in numpy was how do they work can they do broadcasting what does broadcasting mean but one of the problems is okay what do

correct with numpy because correct i've built so much tooling around human uh human interpretability and also

we we're indexing numpy we're just numbers all the columns and particularly the dimensions i mean if you have an image you don't necessarily need to

the scipy and numpy and number and all these projects we already started can pay people to work on them and keep them going so that's labs quonsite labs is a

related oh yeah it was all python numpy consulting kind of stuff um towards the end of that time uh we started getting called into more

is a very pragmatic practical tool uh numpy inherited from numeric and we can get to where numpy came from which is the current array

to think in terms of numpy like yes and that opened up a whole new way of thinking and part of the story that would cut you kind of

so it really helped actually make numpy become a thing so that doc writing that book and it was not a i mean it's not a page

to make money on a book about numpy there's a million stories around that that are positive and those are good to think about

it from the beginning of writing numpy because of this function question like taking the power of arrays is really

example of something i think numpy could add could be added to numpy but one of the challenges again how do you fund this like like i said one of the tragedies i think is that so i i

vectorized was a was a tool in numpy it was released you give it a python function and it gave you a universal function a u-function would work on

and and have them use for numpy arrays like the needed support numpy race and so uh how does it work you have a comment within python that tells to do

make the software better like make numpy better make scipy better make it it's it only works on open source so

draw from you having created numpy and scipy like in service of sort of answering the question of what it takes to be a great

also install some other packages that use different versions of numpy or some like graphics library or some opencv thing or some other thing you now run

myself for small projects and big is when finally when numpy announced that

array library that everybody can use so that's where numpy came from was my thinking hey i can do this and who else is going to because at that point i'd

we i can tell you all the flaws with numpy right you want to go into it there's lots of stuff that i'm like oh man that's embarrassing that was wrong i

letter in 2012 that you wrote uh to the numpy mailing list announcing that you're leaving numpy yeah and what some of the things you've listed

with a new kind of computer like an abstraction that numpy should just operate nicely over the things that are more and more and smarter and smarter

the trade-off between usability and efficiency broadly in numpy but also on these like specific weird quirks of like a single

function yep so on that point if you use a numpy math function on a scalar

because the the math object in p in numpy is more complicated right because you can also call that math object on an array

big long process it takes too expensive it was actually hurting numpy development because i would hear people were saying don't make that change to

scalable like speed and scale uh how do i write numpy code or numpy like code if i want it to run across a cluster you know that's das or maybe

imports some appropriate libraries and say it uses numpy convolution or so i that i was not even aware that exists and it does the appropriate thing

- So when you're on these packages, NumPy, PyTorch, TensorFlow.

how do we have millions of vans full of people that create numpy scipy that create python so you know the story of those people is

overall community sort of had a lot of input into when the numpy core folks decided they would end of life on python 2.

adobe scripting thing i was thinking about you know when we do use something like numpy when we use things in the python data and

writing the c api documentation that went in the first documentation for numpy for numeric sorry this is paul dubois david asher connor hinson and

math operations so the key for me was i was going to write numpy and then move side pi to depend on numpy in fact early on one of the initial