Try solving some Project Euler problems. You'll see the win pretty quickly when you have to search a large range for primes.
With the right algorithm you can solve it in Python in a max of 30 seconds. With the wrong one, you sometimes can't solve it in C++ in the lifetime of the universe.
Therefore you should program in the language that you find it easiest to express yourself in. And not in a language chosen for raw speed.
You'll see the win pretty quickly when you have to search a large range for primes.
This isn't just shifting the goal posts, you are now talking about something completely different.
With the right algorithm you can solve it in Python in a max of 30 seconds
Then you can do the same thing in C++, but when it runs the C++ program will be done 100x faster. A lot of modern C++ is as clean as python if you were being more explicit about types.
With the wrong one, you sometimes can't solve it in C++ in the lifetime of the universe.
Nothing about this makes any sense.
Therefore you should program in the language that you find it easiest to express yourself in. And not in a language chosen for raw speed.
If you continue on with python and need more speed you will run around in circles trying to get it while the C++ version is already done. Anything where speed can be a bottleneck is not fit to be written in pure python.
This pattern plays out over and over again. If something is resource intensive, a scripting language is not a long term solution.
Absolutely none of your arguments make sense in a context of a series of programming puzzles of fixed size. Back in the real world, there is a real need for exploratory programming on mathematical problems. I' prefer to use Python for that. If your problems are statistical, R is a better choice. C++ is a poor fit for exploratory programming.
C++ is a good choice for serious mathematical computing. But not always the best. https://www.hpcwire.com/2020/01/14/julia-programmings-dramat... shows the growing popularity of Julia, even for the most demanding computations.
Looking to the future, I would choose Rust over C++ for most new projects where people currently use C++. It seems bizarre to me that C++ evangelists can talk about type safety and ignore memory safety, when memory safety is a giant problem for security and correctness in any large program.
So, yeah. C++ is great for what it's great for. But if I'm going some exploratory back of the envelope calculations, I'm still reaching for Python. And yield is one of the reasons why.
It works fine for me. Are you sure this isn't just because you don't have a lot of practice with C++?
It seems bizarre to me that C++ evangelists can talk about type safety and ignore memory safety, when memory safety is a giant problem for security and correctness in any large program.
This has nothing to do with the current thread, but this isn't really a problem in modern C++. Are you you sure you are up to date with modern C++? Most programs don't look too different than python due to for iteration and auto type deduction.
I have no idea why you are trying to evangelize C++ here. I use a wide variety of languages for different purposes. For Project Euler type tasks I switched to Python about 15 years ago because of yield, and the convenience of using objects as hash keys. If you make different choices and they work for you, then have fun.
You seem to be saying there is some advantage to python because of the yield feature and I just don't think that's true. You can keep state in a class and call a method. There are a lot of disadvantages though.
triple sigh
I'd say the flexibility to not need to know how big of a prime you'll need is the main draw.
The programming flexibility is indeed the win.
def primes ():
yield 2
yield 3
f = {}
n = 5
for p in primes():
if p == 2:
continue
p2 = p*p
while n < p2:
if n in f:
double_p = f.pop(n)
m = n + double_p
while m in f:
m += double_p
f[m] = double_p
else:
yield n
n += 2
f[n] = 2*pTo do that it recursively contains a second prime generator that only produces up to the square root. Which contains one that produces up to the fourth root. And so on until you get down to 3. The work and memory for those recursive generators is a rounding error compared to the initial prime generator. The work and memory saved by not keeping unnecessary primes is a significant win.