Ok, Python is an interpreted language and therefore it is slower than C. Everybody agree with that.
But despite that, there is an ever-increasing growth of interpreted languages vs. compiled languages.
Perhaps the increased speed of computers and a need for greater flexibility in programming aid in this change.
Ok, Python is an interpreted language and therefore it is slower than C. Everybody agree with that.
But despite that, there is an ever-increasing growth of interpreted languages vs. compiled languages.
Perhaps the increased speed of computers and a need for greater flexibility in programming aid in this change.
Some languages can be interpreted and compiled offering the best of both worlds. In fact there’s a C++ interpreter developed by CERN
Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN.
You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest.
Ok, Python is an interpreted language and therefore it is slower than C. Everybody agree with that.
But despite that, there is an ever-increasing growth of interpreted languages vs. compiled languages.
Perhaps the increased speed of computers and a need for greater flexibility in programming aid in this change.
Some languages can be interpreted and compiled offering the best of both worlds. In fact there’s a C++ interpreter developed by CERN
just saying:QuoteNumba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN.
You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest.
Ok, Python is an interpreted language and therefore it is slower than C. Everybody agree with that.
Ok, Python is an interpreted language and therefore it is slower than C. Everybody agree with that.
But despite that, there is an ever-increasing growth of interpreted languages vs. compiled languages.
Perhaps the increased speed of computers and a need for greater flexibility in programming aid in this change.
Some languages can be interpreted and compiled offering the best of both worlds. In fact there’s a C++ interpreter developed by CERN
just saying:QuoteNumba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN.
You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest.
I have to try numba. It seems a great tool
I’ve used Nuitka in the past for compiling python to machine code, which is great but it can’t be used with jupyter notebooks
If you want to use your time useful, then join the python core development team. You get to program in C and develop these menial tasks by hand.
Other than posting here, saying: " Look it is slow for things that you are not meant to do".
Ok, Python is an interpreted language and therefore it is slower than C. Everybody agree with that.
But despite that, there is an ever-increasing growth of interpreted languages vs. compiled languages.
Perhaps the increased speed of computers and a need for greater flexibility in programming aid in this change.
Some languages can be interpreted and compiled offering the best of both worlds. In fact there’s a C++ interpreter developed by CERN
just saying:QuoteNumba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN.
You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest.
I have to try numba. It seems a great tool
I’ve used Nuitka in the past for compiling python to machine code, which is great but it can’t be used with jupyter notebooksThe basic concept is that you use functions available in numpy arrays for example
When you write the Numba orerators, you will fix the variable type. So when you call A+B it doesn't have to look up which types of A and B is, just uses the "int" A+B for example. And doesn't check for NaN for example.
It varies how much it speeds up the code. I never found a real use case for it, the added code complexity and error messages made the entire process cumbersome. I rather wait for the computer to finish calculations.
That being said the longest I had to wait for python to finish computing was maybe 30 minutes. Machine learning in torch. Good luck writing that in C.
No, Python is not only other tool. Is more popular than the other tools.
Once you have tried Python you will use it again.Now that is just silly.
Python is only a tool among tools.
No, Python is not only other tool. Is more popular than the other tools.
Just silly is having a simple, useful, free, ubiquitous and popular tool and not wanting to use it.
I think being the most popular of all languages must be for some reasons.
That is precisely what I want to know with this thread.
But in this case it is not a trademark and there is no advertisements.
It is counterintuitive for an interpreted language to reach such popularity in a world where speed is so prized.
Most users of programming languages are knowledgeable and smart people who simply do not get carried away simply by popularity reasons to use a language.
It is counterintuitive for an interpreted language to reach such popularity in a world where speed is so prized.You have that completely wrong.
Humans appreciate effortlessness much more than they appreciate speed or efficiency.
Most users of programming languages are near-idiots swayed by passing fads, in my experience over the last three decades. Vast majority, like the rest of humanity, is prone to magical thinking.
It is counterintuitive for an interpreted language to reach such popularity in a world where speed is so prized.You have that completely wrong.
Humans appreciate effortlessness much more than they appreciate speed or efficiency.
Yep. Actually, almost the opposite of efficiency has been witnessed in computing for the last few decades.
https://en.wikipedia.org/wiki/Wirth%27s_law
I think being the most popular of all languages must be for some reasons.
That is precisely what I want to know with this thread.
I think being the most popular of all languages must be for some reasons.
That is precisely what I want to know with this thread.I'm interested in knowing why you want to know? Why is it important to you? What does it matter to you if something is popular?
Mandarin Chinese is the most popular of all spoken languages in the world. Are you interested in learning Mandarin Chinese? If not, why not?
Perl has better libraries
Ruby outperforms Python in several ways.
I'm at the opinion that Python's success is only partly related just to the language itself.
The whole Python ecosystem - where you'll easily find a package for nearly everything with reasonable quality, mostly good documentation that's well integrated - is unprecedented IMHO and substantially adding to it's popularity
const ARRAYSIZE = 10000000;
function partition(values, left, right, pivotidx) {
let pivot = values[pivotidx];
var temp = values[right];
values[right] = values[pivotidx];
values[pivotidx] = temp;
var storeidx = left;
for(var idx = left; idx<right; idx++) {
if (values[idx] < pivot) {
temp = values[idx];
values[idx] = values[storeidx];
values[storeidx] = temp;
storeidx++;
}
}
temp = values[storeidx];
values[storeidx] = values[right];
values[right] = temp;
return storeidx;
}
function _doquicksort(values, left, right) {
if (right > left) {
var pivotidx = Math.trunc((left + right) / 2);
pivotidx = partition(values, left, right, pivotidx);
_doquicksort(values, left, pivotidx);
_doquicksort(values, pivotidx + 1, right);
}
return values;
}
function quicksort(nums) {
return _doquicksort(nums, 0, ARRAYSIZE-1);
}
var nums = [];
for(var i=0; i<ARRAYSIZE; i++) {
nums[i] = Math.random();
}
var timeit = Date.now();
nums = quicksort(nums);
timeit = Date.now() - timeit;
console.log("time = %f\n", timeit/1000);
bruce@rip:~/programs/qsort$ node qsort.js
time = 1.415