Laziness in Python - Computerphile

[@not_proton]

This guy looks like every computer scientist to ever exist, in the same body

[@NuclearSlayer52]

the Avatar

[@JackVermicelli]

I immediately thought of Brent Spiner's character in Independence Day.

[@erinphilippe5993]

@@JackVermicelli same!

[@noobian3314]

🤣🤣🤣🤣🤣🤣🤣🤣🤣

[@isbestlizard]

Yesssss and talks like Dr Strangelove

[@EnigmacTheFirst]

4:10 “3+4 is... nothing. Oh, it’s 7. Okay, that’s good” I don’t know why this had me laughing so hard

[@dudeabideth4428]

That made me laugh too. Like a smart professor struggling with a gui interface

[@ahmedfenti9462]

Bacon u making lthings boring

[@Robstafarian]

My face hurts from laughing at that.

[@hjp360]

same with me.

[@Twisted_Code]

and then he messed up trying to call nats, not originally expecting it to return and iterator "that was not very clever". They got a kick out of me, sort of reminding me I'm not the only one that occasionally forgets these things. Thanks for leaving that one in @Computerphile

[@WigglyWings]

I don't know about coding but I am kind of an expert in Laziness.

[@worthlessguy7477]

brambedkar59 and you’re not alone

[@deus_ex_machina_]

Indeed. Not to brag, but I'm one of the foremost experts in the field.

[@Twisted_Code]

if IFeelLikeIt: yield work

[@nobodyofconsequence6522]

To be fair a lot of coding is about laziness, so you probably have an aptitude of it.

[@Twisted_Code]

@@nobodyofconsequence6522 this is... surprisingly true. Especially when you get to the point of trying to make your program more efficient. You're really just trying to get it to do as much with as little time as possible, and sometimes that may be through laziness of not handling something that's out of the scope of the original program design. Basically saying "not my problem; I'm just going to do what I was told to do, nothing more". And, surprisingly, limiting the scope like this actually makes future programming more modular. In other words, it works.

[@ThorstenAltenkirch]

Ok since I am at it here is another comment: no the sieve of Erasthostenes is not an efficient method to compute primes, I just used it as a way to illustrate computing with infinite datastructures. It is known since a while that Primes is in RP, that is randomised polynomial time. Some years ago it was even shown to be in P, i.e. you don't even need a dice.

[@galvanart3623]

Professor, could you then kindly suggest a more efficient method of code, to find primes?

[@jens6398]

The sieve is efficient. But you implemented trial division.

[@ThorstenAltenkirch]

@@jens6398 I don't know what you mean by "trial division" but I have implemented the sieve.

[@jens6398]

@@ThorstenAltenkirch No. There is *never* any divisibility test in the sieve. Instead there is elimination of multiples, as you actually explained in the beginning of the video. To quote Wikipedia: Primes can also be produced by iteratively sieving out the composites through divisibility testing by sequential primes, one prime at a time. It is not the sieve of Eratosthenes but is often confused with it, even though the sieve of Eratosthenes directly generates the composites instead of testing for them. Trial division has worse theoretical complexity than that of the sieve of Eratosthenes in generating ranges of primes.

[@arirahikkala]

I hope KZbin won't completely mangle my code here. I grabbed a convenient library that provides a `primes` in Haskell and set out to calculate, on the one hand, the count of times that a number is crossed out when finding all the primes less than some given n using the sieve of Eratosthenes: > sieved n = sum . map (\p -> length . takeWhile ( length . takeWhile (\p -> c `mod` p /= 0) . takeWhile (

[@Erik_The_Viking]

Professor Altenkirch is hysterical. I wish I had him during my CS classes.

[@AWES0MEDEFENDER]

Hilarity?

[@SirFancyPants21]

Serdar Kocak hysterical is often used as “even more than hilarious”

[@Zoronoa01]

@sirati97 he means hilarious

[@chrisbreidenstein3831]

Lol take it easy

[@Zoronoa01]

@@chrisbreidenstein3831 he meant hilarious

[@chaosphoenixhex]

Dude, I feel so relaxed just looking at this guy. It’s a true inspiration, thank you. I need a beer now.

[@alexstabell3064]

This is honestly some of the most beautiful code I've ever seen. I've made a many sieve, but never a lazy sieve. I can't believe you can do this incredible calculation with only a few lines of code and an understanding of coroutines.

[@SirKickassington]

Agreed. He is the most likable professor ever. And in keeping with the important lesson on laziness, I’m typing this lying down.

[@Jeacom]

me: nooo, you can't define a recursive function without an exit condition!! Altenkirch: haha, stack goes brrrrr.

[@MubashirullahD]

haha. We could have done for i in range(n): yield i

[@ThomasBomb45]

@@MubashirullahD That's basically a circular definition. Range() needs to be defined as well

[@MubashirullahD]

@@ThomasBomb45 I don't know what you mean by circular? :) I'm assuming everyone knows range is lazy in python 3.

[@ThomasBomb45]

@@MubashirullahD because his nats() function's output is exactly the same as range() except it is infinite. Rather than use range internally, he explicitly calls yield to demonstrate how it works

[@y.z.6517]

@@ThomasBomb45 # Range is not needed i=0 while True: i+=1 yield i

[@justingolden21]

TLDR: "yield" in python is used in generators to loop through something and store the data lazily so when you call the function next it doesn't have to start at the beginning but rather picks up where it left off (often in a loop)

[@phillip5187]

Ty!

[@jayachandra677]

No you cannot multiply strings python: print("haha string goes b" + "r"*10) >haha string goes brrrrrrrrrr

[@SteelSkin667]

I'm stealing that

[@Excgo]

I see, you have been on r/programmerhumor today.

[@hamiltonianpathondodecahed5236]

alternatively print('ha' * 2 + 'string go b' + 'r' * 10)

[@theRealChadWarden42069]

reddit

[@righo96]

this is str*int, not str*str

[@TotalImmort7l]

Laziness in python is writing codes without getting annoyed by ';' and understanding the beauty of a < x < y < b.

[@omri9325]

Lex Fridman

[@TotalImmort7l]

@@omri9325 yep, he described this feature of python as the most beautiful thing a programming language can ever have.

[@mibdev]

I dislike python but love a < b < c It's genius.

[@ZipplyZane]

I know I could look it up, but I'd love for you to tell me what that does. Does it just test all those conditions, i.e. returning true only if a is less than x, x is less than y, and y is less than b?

[@TotalImmort7l]

@@ZipplyZane See it in this way: Given expression: a < x < y < b 1. First, the interpreter evaluates (a < x). Let's say, the result of "a < x" is True. Then the current expression will be True < y < b. As in #1, the interpreter evaluates (True < y), holds the result in a stack, and repeats until the interpreter reaches the end of the expression. Don't think it in terms of stacks and ASTs. It is better to see this as a mathematical expression.

[@tobyclark6312]

I hadn’t considered Nottingham as a potential university before but just looked at it now and will definitely be considering it as a first/second choice!

[@sharkinahat]

I guess I'll be That Guy today: "Now is better than never. Although never is often better than *right* now."

[@aragonnetje]

The zen of python is never a bad thing to reference

[@cheaterman49]

So Zen! :-)

[@Twisted_Code]

>>> import _this_

[@y.z.6517]

'now' == 'right now'. 'Right' is just a stress with no real meaning. You sentence is basically 'Now is better than never. Never is better than now.' This can be reduced to False.

[@Twisted_Code]

@@y.z.6517 I think you might've been staring at your IDE too long

[@tylersanders6854]

@1:30 he can say the word "really" without moving a single muscle.

[@luismanuelricogutierrez6863]

This video is all about laziness, I guess

[@simontungisawesome]

Laziness lol

[@VivekPayasi]

This cracked me up :D

[@luciojb]

- "WHEHRE IS THIS THING???" - "ok ok yeah yeah imma do it" Best lines ever

[@JacobCanote]

This is a perfect example of real life coding.

[@ZeedijkMike]

The content is great and his warm dry humor makes me giggle.

[@gedtoon6451]

This is a good explanation about yield. With regard to Sudoku, all good Sudoku puzzles only have one solution.

[@dooza]

I'VE WATCHED SO MANY VIDEOS FROM COMPUTERPHILE AND NEVER SEEN THE VOICE BEHIND THE CAMERA. FINALLY.

[@trueriver1950]

This is because with social distancing via Zoom (or similar) there is already a camera on the interviewer. Pre-Covid it would be non-lazy to add a second camera pointed at the camera operator. Or to put it another way, in a video conference everyone is a voice behind a camera, including the professor

[@bingloveskoki]

Just wanted to say that I really enjoyed your short paper about the contribution of Martin Hofmann. Thanks!

[@ConnorKennedy16]

I think it's between Altenkirch and Brailsford as my favorite overall Computerphile presenters in terms of raw depth-of-content. Pound is a close third.

[@TheRuralpoet]

Enjoyed this... He had a great sense of humour... Intellect with humility is the greatest... Thanks

[@MegaArti2000]

This yield statement puzzles me so hard man

[@raingram]

It's like "return" but it starts from that line the next time you run the function. It just "pauses" at that point, waiting for you to call it again.

[@KbIPbIL0]

yield is multiple return yield makes your function "G" a generator, which yields a sequence. when you call next(g) it continues running until the next yield statement and the function next() returns the thing you yielded.

[@christophebeaulieu4916]

@@raingram I've literally never seen it that way. You're amazing. Thanks for your comment. I get it now

[@hanvyj2]

@@raingram great explanation

[@jphanson]

@@christophebeaulieu4916 ~. i’m

[@code-dredd]

"I wanted to talk about laziness, but I was to lazy to do talk about it" Missed opportunity 🙃

[@y.z.6517]

You are not lazy, or you wouldn't have commented.

[@Saver006]

Trying to show your homies a cool programming trick be like: ''...so now when I press this button... oh it doesn't work...yyym'''

[@nekkowe]

I'm always happy to see a new episode with Prof. Altenkirch :>

[@CryTaxel]

Doesn't this create a callstack that gets bigger and bigger with every iteration (like all non-tail-recursions) or am I missing something?

[@noobinvestor3180]

Aalex not quite actually..generators or iterators in general holds the address to the particular value it is now referencing and moves to the “next” address when next() is called

[@chris8443]

Yes. Also, python doesn't support tail recursion. Edit: as pointed out, of course python supports tail recursion. :-) What I meant is that it doesn't support _tail call elimination / tail call optimization_.

[@Schnorzel1337]

Tried it with the exact code and for default settings python 3.7 it gave an "RecursionError: maximum recursion depth exceeded" after the prime 733.

[@sadhlife]

@@noobinvestor3180 it will, the yield from is the issue here, it's basically recursion

[@sadhlife]

@@chris8443 not yet :)) with the new PEG parser, we might get something nice

[@ahmadalkhansa5294]

For a moment I thought I didn't understand seive but suddenly profesor corrects it.😅 It's beautiful to show that it's ok to make mistakes

[@rooneye]

You can tell this guy knows what he's talking about here. He looks like an EXPERT in the art of lazy.

[@raqha4575]

i love his coffee mug

[@RichardHamnett]

This method is paramount for feeding data / data augmentations into machine learning model training

[@MecchaKakkoi]

The shirts are always top shelf!!!!

[@StreuB1]

4:26 'nutz' 4:40 'yeet' Got it Prof. Thorsten is friggin awesome.

[@shawon265]

This is probably the shortest pythoniest-python program I have ever seen!

[@kkloikok]

"oh it's ... That's bad" Story of my life.

[@SuperBartles]

"This is what being a programmer is actually like" tutorial

[@xario2007]

Never knew about yield - yet I use Haskell and Python. Very helpful!

[@TheDhammaHub]

Laziness as in "waiting to evaluate an expression as late as possible"? Or laziness as in getting nothing done? xD

[@SteelSkin667]

The latter is much easier to implement!

[@__.__-_.]

pull request let me do nothing all day

[@Argon270]

IKR! There are 2 types of laziness in programming.

[@GifCoDigital]

I now finally see the big advantage of using generators!!! Great video!!

[@rz2374]

I know i am 39 minutes late but I have never been this early before. Seeing only 62 comments is so weird. And this is nicely timed as I was just researching about yield statements.

[@RUFU58]

When you’re not programming you’re guarding the train in The Matrix. Top man!

[@TheJaguar1983]

As soon as I see the title, I figured it would be about generators. Very useful things.

[@TerjeMathisen]

The Sieve code is interesting, but I really worry about the efficiency! There are only two ways to generate the next prime here, and that is to either remember all previous primes, and iterate until you find one which is not a multiple of any of them (but only up to sqrt(n)), or you have to use standard sieve thinking and strike out at least a bunch of multiples each time you find a new prime. The second approach has much more useful Big-O properties. The Sudoku solver otoh will work with constant memory, it should be more or less equal to a standard recursive solver with backtracking?

[@anindyasundarmanna6683]

This video is absolute gold!

[@timseguine2]

I could be wrong, but I am pretty sure this will blow the stack if you do this in python

[@pedrovasconcelos8260]

There is a fundamental difference between iterators and laziness: lazy evaluation memoizes results of subcomputations whereas iterators do not. For example, the Haskell version of the infinite list of Fibbonacci numbers can be defined as fibs = 0:1:zipWith (+) fibs (tail fibs) If you translate this definition to iterators you'd end up with something like the following: def fibs(): yield 0 yield 1 yield from (i+j for i,j in zip(fibs(),tail(fibs()))) def tail(it): # couldn't find this in the standard library... next(it) # drop the first element while True: n = next(it) yield n However, the Python code above takes exponential time whereas the Haskell one takes linear time (ignoring the log factor for the increasing sizes of numbers). To get linear time using iterators you have to manually code the memoization (using a loop for simplicity): def fast_fibs(): a,b = 0,1 while True: yield a a,b = b,a+b

[@pedrovasconcelos8260]

@@zzzdenda Lazy evaluation a.k.a. call by need requires memoization of results, otherwise it is simply call by name. For reference check the chapter on graph reduction i. SPJ's "The Implementation of functional languages" (it's out of print by freely available from his web page).

[@yokilewis4894]

8:19 This kind of naming always gets me. In Haskell, when doing `filter`, do you filter out the elements that make the predicate true, or do you keep them? (It takes a while to get used to it!) Similarly, do you sieve out the elements, or are you actually keeping them?

[@darcipeeps]

Same thing as other search filters

[@y.z.6517]

Always run a 2-line test code first to figure stuff like that out. This is even more important, if you dip in and out of several languages and libraries.

[@AzraelCC]

I don't understand why Computerphile videos would have any dislikes. That said: NICE.

[@beskamir5977]

"Laziness is a virtue - well, in programming anyway!" No need to specify that it's in programming. Without laziness we wouldn't have had a reason to domesticate animals, build societies, develop science and technologies, create machines, etc. Basically we'd still be in the stone age if our species wasn't lazy and constantly looking to make life easier for ourselves.

[@beskamir5977]

@Joseph Malone I disagree. By using an animal to plow your field (and later a machine) we decrease the work we need to do. Ideally we wouldn't be doing that job at all but at least until we get decent enough ai, not doing that job would mean we wouldn't get to eat. Therefore laziness is expressed as "optimization" rather than as completely not doing the required task because the task is essential for our survival. In the case of tasks that are less essential for our survival such as copying books, the laziness factor is more subtle as the motivation there is more about saving costs than not doing the original job. Although saving costs can be considered as part of laziness since it allows those involved to get the same thing for less effort. Essentially minimizing effort is inherently linked with and usually driven by laziness.

[@cryonim]

@Joseph Malone As a character's voiceline in R6 goes, "Efficiency is clever laziness".

[@y.z.6517]

@@beskamir5977 Explain why people play games? By playing games, we basically seek *more* work that are unnecessary.

[@y.z.6517]

@@beskamir5977 Our brain tells from fun and not fun works. Hunting is inherently fun to our brain (or our ancesters would have died from laziness). When our ancesters started ploughing, they avoided the pain of starvation, but doing so also deprived them of the fun of hunting. Most modern works are boring, because our brains were not evolved to do that. BTW, many women enjoy shopping, because shopping is basically paid gathering.

[@beskamir5977]

@@y.z.6517 I think you basically answered your own question. Laziness happens when the thing we need to do is boring. When it's fun laziness doesn't apply as much.

[@ElizaberthUndEugen]

If you use the `filter` predicate, it gets even more obvious that the sieve function precisely mimics the idea of layers of sieves (aka filters!). def sieve(gen): i = next(gen) yield i yield from sieve(filter(lambda k: not is_multiple(k, i), gen)) This evaluates to filter(lambda k: not is_multiple(N, i), ... filter(lambda k: not is_multiple(5, i), filter(lambda k: not is_multiple(3, i), filter(lambda k: not is_multiple(2, i), gen))) ...) where N is the nth prime.

[@nerminskenderovic]

Now how do you write this simple sieve generator function in JavaScript?

[@pafnutiytheartist]

The problem with this approach is that it reaches recursion depth pretty fast. I was able to sieve primes using a slightly improved version of this method to about 3500. After that yield from is just unviable

[@guyindisguise]

It's possible with mutable state instead of recursion e.g. def nats(n): while True: yield n n += 1 edit: fixed typo

[@CecilWesterhof]

That was the first thing I thought. It can (on my system) go to 130, but with 131 I get a RecursionError. Do you mind sharing your code?

[@pafnutiytheartist]

@@CecilWesterhof I did the same thing as in the comment above for generating natural numbers and the rest is exactly like described in the video. I'm not sure how to use mutable state for the sieve itself.

[@CecilWesterhof]

@@pafnutiytheartist That is strange. With that change it goes from 130 to 497. Not nearly the 3500 you talk about.

[@justjoshingaround]

Minor suggestion. I think he should have tried to show what happens at the end of his yield sudoku solver. Using "return" with "yield" has interesting behavior with a "StopIteration" exception.

[@clayz1]

Write a prime factoring routine and apply it looking for remainders of n (the number currently being tested).

[@flyingsquirrel3271]

If you want a lazy natural number generator that doesn't blow the stack after a few iterations, do it manually: class Nats: def __init__(self, n): self.n = n - 1 def __next__(self): self.n += 1 return self.n def __iter__(self): return self Now you can use "Nats(2)" just like he used "nats(2)" in the video. Of course the sieve generator still reaches maximum recursion depth pretty quickly, but it gets way further with this Nats implementation. Edit: I just noticed that "blowing the stack" isn't quite right here. Since it's python, it's all safe and heap allocated anyway, so it just hits the recursion limit.

[@corynivens6510]

You don’t even need a class here: def nats(n): while True: yield n n+=1 Or better yet def nats(s): yield from range(s, sys.maxsize) And lastly: s = range(2, sys.maxsize)

[@flyingsquirrel3271]

@@corynivens6510 Haha you're right. The while True solution is much cleaner. I don't like the sys.maxsize thing though. I know its usually gonna be big enough, but theoretically python ints can grow infinitely big (until your ram is full), so that implementation is technically not equivalent to the other ones.

[@flyingsquirrel3271]

Lol I just looked through the itertools module and of course this iterator exists in the std lib! So here's the definite solution: from itertools import count (then use "count" instead of "nats")

[@corynivens6510]

Anything higher than sys.maxsize will result in an overflow. The only caveat with s = range is that range doesn’t support next(), so you’d technically need s=iter(range...)

[@corynivens6510]

Ah man, the itertools solution really is the one isn’t it? itertools or nothing haha

[@haroldostenger5160]

hi, the cribe is a nice approach, but it fails with recursion error although I expected it not to fail because of the yield from. I'll try to tweak it and fix it.

[@TheGitGuild]

Now I get why developers love Python :)

[@noartifice]

The nats generator shows is mathematically more elegant, but it will not render an infinite sequence since python will create a call stack and you'll eventually receive a RecursionError. itertools.count would avoid that or a while loop

[@kartoffelmozart]

But this way of nesting generators inside of generators will quite quickly give a RecursionError will it not? Because you make another nested generator for every prime you find

[@johnbennett1465]

Generators are great. Using recursion to generate a sequence of integers is horrible! It is very wasteful of both space and time. It will also blow out your stack. He clearly lives in the world of theory where performance does not matter at all. Here is a better version. def count(n): while True: yield n n = n + 1 Simple and efficient. Recursion is a great technique when needed, but it should be avoided when it is not needed.

[@hodsinay6969]

I tried to use something like your count with his "sieve" and it still blew out my stack. I wrote in another comment a better version (in my opinion) of calculating primes. I absolutely agree with your end statement that you should avoide recursion when possible

[@johnbennett1465]

@@hodsinay6969 his sieve has the same problem. I didn't feel like rewriting his whole program on a tablet. I will try to find your comment and check it.

[@lawrencedoliveiro9104]

Try solving this without recursion: computing all the permutations of a sequence. def permute(seq) : "generator which yields successive permutations of the elements of seq." if len(seq) == 0 : yield () else : for i in range(0, len(seq)) : for rest in permute(seq[:i] + seq[i + 1:]) : yield (seq[i],) + rest #end for #end for #end if #end permute >>> list(permute((1, 2, 3, 4))) [(1, 2, 3, 4), (1, 2, 4, 3), (1, 3, 2, 4), (1, 3, 4, 2), (1, 4, 2, 3), (1, 4, 3, 2), (2, 1, 3, 4), (2, 1, 4, 3), (2, 3, 1, 4), (2, 3, 4, 1), (2, 4, 1, 3), (2, 4, 3, 1), (3, 1, 2, 4), (3, 1, 4, 2), (3, 2, 1, 4), (3, 2, 4, 1), (3, 4, 1, 2), (3, 4, 2, 1), (4, 1, 2, 3), (4, 1, 3, 2), (4, 2, 1, 3), (4, 2, 3, 1), (4, 3, 1, 2), (4, 3, 2, 1)]

[@WilliamAndrea]

Also, don't reinvent the wheel. "itertools.count" already exists and does the same thing.

[@formbi]

some languages are smart and flatten the recursion, maybe the professor is accustomed to those

[@mastershooter64]

laziness is me writing a for loop to make a list of alphabets or numbers instead of actually typing it out myself. in a few years I'm gonna start making a neural network that can write half of the code for me lol

[@cn-ml]

Is "yield from y" just syntactic sugar for "foreach (x in y) yield x"?

[@emilejetzer7657]

I see a lot of comments on how this implementation of the sieve is poor; that’s because it’s a simple example of a technique that can be useful. It’s not meant to be used exactly as shown, but it can do the job anyway. The sudoku solver shows a better use case.

[@helloworld9018]

Agreed. Some people just don't get the meaning of the video. Professor didn't say he is going to do 100% accurate implementation. He wanted to show what you can do with yield and how to be 'lazy'.

[@ShaunakDe]

if we kept this running, would the program only halt when the intermidiate variable became too large? Or would it halt before that due to python overheads ?

[@DrGreenGiant]

Would the equivalent in CPP be using statics?

[@PeterAuto1]

no, it's the same as the in c++20 added coroutines

[@DrGreenGiant]

@@PeterAuto1 ah ok. I'm still back in C++11 with what I do

[@lattice.d]

8:04 - 8:15 programming in a nutshell

[@elorz007]

Another way of understanding "yield from" is thinking that it "returns" a function but you didn't call it yet. (Useful concept to also understand function pointers / blocks / closures / lambdas). Kind of.

[@liluziskrrt4116]

Best language ever

[@alexrobertson35]

oh no you're gonna start a war (btw c++ best (: )

[@alexrobertson35]

@Pi Pony I was using smart pointers!

[@alexrobertson35]

@Pi Pony tbh most of the time I don't even use smart one

[@alexrobertson35]

@Pi Pony nope, I probably should but I'm currently learning haskell to get a sense of fp

[@alexrobertson35]

@Pi Pony functional programming. its a paradigm, I'm just interested in it because its so different to what I'm used to with c++ and the bit of python and c# I know. edit I don't think haskell can do more, I'm just learning it out of interest

[@rohangaonkar8912]

can this be done with javascript?

[@jimmybleron4700]

what happens when you reach the end of the stream?

[@MrBoubource]

I've seen you get a recursion error at prime 733. Is there a way to make it efficient ? Else it would be an elegant but useless piece of code

[@CoyMcBob]

See my comment. This is an elegant but useless piece of code. Even if recursion wasn't an issue this is not using a sieve and is painfully inefficient and almost laughably incompetent

[@emilejetzer7657]

Coy McBob can you describe how you would implement the sieve without testing divisibility?

[@Ekevoo]

This mug. I love this mug. I want this mug.

[@atifachaudhry]

The following generator based version of sieve() doesn't have stack overflow and is more efficient. Plus, IMHO, it explains what't going on in the algorithm more clearly; but is a bit longer. def sieve(n=2): prev, skip = [], False while True: for p in prev: if n%p == 0: skip=True; break; if not skip: yield n prev.append(n) n, skip = n+1, False

[@justahker3988]

You could also add in the fact that any composite number n must have a factor 2

[@Twisted_Code]

Why doesn't nats crash when it hits about 1000 or so? Seems like it would hit the stack limit, unless I'm misunderstanding how the control flow works (or unless I mis-implemented it) Edit: nevermind, I must have mis-implemented it. I kept trying various explanations (such as tail recursion) but eventually just tried it again about a week later.

[@helloworld9018]

Speaking about sudoki solver with yield, will next solution always be unique? Or at some point it can return a duplicate of previously found solution? How can one control this process?

[@tobiastriesch3736]

Props for rocking the unicorn mug 🤩

[@lennyschwarz6683]

9:12 The "Three!" is perfect

[@davidwallis4281]

nats() seems very 'functional', but it contains state, and the function returns a different value with the same (no) arguments which seems the opposite to what functional often tries to do. Still learning...

[@mennoltvanalten7260]

'if I yield long enough, I will get all the numbers' Will you not get a recusion depth exceeded error?

[@ThorstenAltenkirch]

I have an infinite stack.I made it by using the tape from a Turing machine and cut it in the middle. :-)

[@WadelDee]

Can you also say previous(s)? Because that might enable you to create infinite structures that can go infinitely in both directions!

[@stephaned198]

Why is like he does'nt sleep enough ?

[@Argon270]

Because too lazy he isto sleep . I'm sorry! I make grammatical errors too

[@appleslover]

Lazy to sleep

[@neutronstar6739]

Lazy sleep

[@sameash3153]

I haven't slept well in years

[@TrippSaaS]

If computer scientists were Pokemon, this guy is the final evolution.

[@netslayeruk]

Having a hard time understanding this. For the seive, he is typing next and he's running the function... For 2 then 3 then 4. Is he doing this just to reach the solution for 4? What's the advantage over running the original function with 4 as a parameter?

[@thinboxdictator6720]

it suppose to show just primes if you start with 4,it won't remove multiples of 2 and 3 : >>> from sieve import sieve , nats >>> p = sieve(nats(4)) >>> next(p) 4 >>> next(p) 5 >>> next(p) 6 >>> next(p) 7 >>> next(p) 9 >>>

[@nicolozorzetto3871]

NetSlayerUK in that case no advantage. In the second example (prime numbers) the syntax is a lot simpler and human readable instead of nested loops

[@netslayeruk]

@@thinboxdictator6720 With the original function you could put in X and it would sieve from integers '2 to X' and return the sieved contents of prime numbers up to X. I'm just stumped on how this is different.

[@harrywang9375]

This dude looks like the perfect guy to describe laziness

[@superwinner5010]

Hah, a few weeks ago I got interested in prime numbers and after some time I got it to be able to rival C in speed, best I got it was all primes under 10^11 in 93.6s which is blazing fast, but I could have still made it even faster, but it gets very complex fast.

[@dh00mketu]

How is s an iterator?

[@dh00mketu]

Dude just remove the yield nats(n) and you will know.

[@BlitzPSH]

11:09 I don't think you need global here. Global is only needed if you're changing a reference variable in the outer scope, which you're not doing when simply mutating a list.

[@user-vn7ce5ig1z]

If you have to learn some really unusual syntax before you can do this, then does that still count as lazy? 🤔

[@WilliamAndrea]

It's not that unusual... Just generator expressions, "yield from" and recursion. IMO, assignment expressions and the "generator.send" protocol are much weirder.

[@Avicenna697]

- Yes, it does. That’s kind of the hidden joke in the definition of ‘laziness’: that you can spend much longer looking for the shortcut than it would have taken to go the long way round. The payoff is that you can use the same shortcut (or programming trick) multiple times (eg for serving multiple users) or for more complex problems (where the long way round rapidly becomes impractical) in the future.

[@PyPylia]

One quick question, does anyone actually use jupyter in a professional job? I've only ever had to use the actual python interpreter, the only IDE I ever use is visual studio code.

[@LeeC24535]

Define what you mean by “in a professional job”? I work as a SWE and I’ve seen plenty people use Jupyter notebook for their own testing and playing around. That’s the only time I’ve seen it be used honestly.

[@PyPylia]

@@LeeC24535 Huh, I've never really seen it as the actual interpreter is always quite a bit faster and is just as easy to get up and running.

[@jeprox120]

I’m an Electrical Engineer in Semiconductor business. I’ve used Jupyter notebooks as part of analyzing characterization data. Answer to your question depends on definition of “professional”.

[@gathering2]

The generator breaks after too many primes because of maximum recursion depth, so I guess this example is more of a theoretical one to show that infinite structures still can be implemented using iterators?

[@ChaoticRain1]

I was wondering about this as well. Are there any practical examples of when you would use a generator/yield? Once the numbers get into the millions, wouldn't this take an insanely long time to get the next Prime?

[@jesusjar11]

@@ChaoticRain1 Just use tail recursion

[@PaulaBean]

PEP8: no spaces before colons, and put spaces around your operators.

[@KbIPbIL0]

colons are not operators, no?

[@hodsinay6969]

when calculating primes the way you said in python it will do it layzily, but it will probably not have enough space in a matter of seconds, generators are not something that you just save an infinate amount of without repercussions, neither are levels of recursion. I suggest to get the primes lazily with something more like: def get_primes(): primes = set() i = 2 while True: if all(i % p != 0 for p in primes): yield i primes.add(i) i += 1 this code does basiclly the same as the code in the video (even though there are probably better ways to calculate prime numbers) I know that it shows the power of generators less, but it probably is a bad idea to use a generator that uses a generator that uses a generator...

[@johnbennett1465]

As promised, I came over here to check it out. This is much better than his. It would be nice to not save all the primes you already found, but I can't think of anyway to avoid it.

[@hodsinay6969]

@@johnbennett1465 there are a lot of ways to improve this algorithm of getting primes. for instance not checking all the primes but only the ones that are lower than the number sqrt (and saving the primes in a list for that), or just checking all the numbers up to the number I am checking sqrt's, in the long run it is probably better. I just wanted to implement the same algorithm as he did without using unneeded recursion.

[@slash_me]

@@hodsinay6969 you could make it a little more pythonic like this: import itertools def get_primes(): primes = set() for i in itertools.count(2): if all(i % p != 0 for p in primes): yield i primes.add(i) That also brings back the usage of generators: itertools.count is a generator that counts to infinity!

[@hodsinay6969]

@@slash_me that's a great suggestion, I didn't know this generator before...

[@johnbennett1465]

@@hodsinay6969 your suggestions do improve the speed. I was more worried about running out of memory. Your suggestions don't help with that. Admittedly you have to create a lot of primes before it is a problem. 🙂 I just don't like programs that keep eating up memory and try to avoid creating them when I can. Having to port my spell checker to a microprocessor with only 256 bytes (yes bytes) of RAM memory may have scared me. 😅 Note that the code and compressed word list was in ROM.

[@2rfg949]

haha I thought you wrote that wrong!! but I was deferring to your professorial nature! I love you videos

[@2rfg949]

the coolest thing i got from this is modularity. the rest is fascinating, i did not know yield, and recursion does my head in a bit, but i can use it. but modularity is such a level up for me in my coding,. thank you for that my friend!!!

[@rodizadi9591]

Never seen a man who speaks so well without moving their lower jaw

[@darcipeeps]

Just learned about this yesterday in my programming languages course

[@sarwagyajain4841]

With recursion in generators does the call stack keep growing (especially in the case in the video) or does it optimize and keep only the relevant variable?

[@siliconmessiah1745]

I don't know if it for this video specifically, but professor seems to bring about an air of laziness through his mannerisms as well😂

[@ImaginaryMdA]

won't your call stack just explode before long, though?

[@TechyBen]

(Looks at my cat sleeping) Nature is "efficient", just turns out efficiency is comfortable sometimes. :)

[@christinebraun9610]

0:15 is me anytime I get an email from my university admin

[@dalirkosimov4623]

🤣🤣😩