Re: [python-uk] Fwd: It Will Never Work in Theory: live!
thanks a lot regards Le ven. 4 mars 2022 à 23:54, Alex Willmer a écrit : > Each email from this list has a link to the listinfo page at the bottom, > https://mail.python.org/mailman/listinfo/python-uk. This is true of all > python.org mailing lists. > > Follow the instructions there to change your options, or unsubscribe. > > On Fri, 4 Mar 2022 at 20:52, BELAHCENE Abdelkader < > abdelkader.belahc...@enst.dz> wrote: > >> hi, >> I receive a lot of email from the python-list, I want to disable it, when >> I want to read the email, I want to go to he List. >> Please How to disable it. >> Regards >> > ___ > python-uk mailing list > python-uk@python.org > https://mail.python.org/mailman/listinfo/python-uk > ___ python-uk mailing list python-uk@python.org https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] Fwd: It Will Never Work in Theory: live!
hi, I receive a lot of email from the python-list, I want to disable it, when I want to read the email, I want to go to he List. Please How to disable it. Regards Le jeu. 3 mars 2022 à 18:05, Steve Holden a écrit : > A communication from my good friend Greg Wilsin (instigator of the > Software Carpentry workshops) which may be of use to some. It certainly > looks like great value for money. > > Kind regards, > Steve > > > -- Forwarded message - > From: Greg Wilson > Date: Tue, Mar 1, 2022 at 5:11 PM > Subject: It Will Never Work in Theory: live! > To: Steve Holden > > > Hi Steve, > > On April 27, It Will Never Work in Theory is running two sets of online > lightning talks from leading software engineering researchers in which > they’ll summarize actionable findings from their work for practitioners. > Tickets are now on sale at https://neverworkintheory.org/, and all money > raised will to go Books for Africa. I hope to see you there, and if you > could help spread the word or help sponsor it by matching money raised > from ticket sales, we'd be very grateful. > > Cheers, > > Greg > > ___ > python-uk mailing list > python-uk@python.org > https://mail.python.org/mailman/listinfo/python-uk > ___ python-uk mailing list python-uk@python.org https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
Thanks,
But the different is very significant!!!
so?
Le ven. 25 févr. 2022 à 10:58, Edward Hartley a
écrit :
>
> Hi,
> Simply check the original Numpy aka Numerical Python docs where it’s
> comprehensively explained that the library is implemented in C with a thin
> Python wrapper. The docs were written circa ‘98 by the original library
> author. The library was optimised over a long period before being released.
> I’ve seen similar results when replacing an optimised Fortran library with
> C/C++ the key is the effort given to optimisation, and care with avoiding
> unnecessary memory allocation.
> I’ll dig out the reference when I’ve tracked it down.
> Best of Luck
> Ed Hartley
>
> On 25 Feb 2022, at 09:42, Giorgio Zoppi wrote:
>
>
> Well,
> numpy is written in C :) Maybe your C is not the numpy equivalent?
> Best Regards,
> Giorgio
>
> Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader <
> abdelkader.belahc...@enst.dz> ha scritto:
>
>> Hi,
>> a lot of people think that C (or C++) is faster than python, yes I agree,
>> but I think that's not the case with numpy, I believe numpy is faster than
>> C, at least in some cases.
>>
>>
>> *Is there another explanation ?Or where can find a doc speaking about
>> the subject?*Thanks a lot
>> Regards
>> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is
>> an example Let's look at the following case:
>> Here is the result on my laptop i3:
>>
>> Labs$ *python3 tempsExe.py 5*
>> sum with Python: 1250025000 and NumPy 1250025000
>> time used Python Sum: * 37.28 sec *
>> time used Numpy Sum: *1.85 sec*
>>
>> Labs$ *./tt5 *
>>
>>
>> * CPU time :7.521730The value : 1250025000 *
>>
>>
>> This is the Python3 program :
>>
>> import timeit as it
>> import numpy as np
>> import sys
>> try :
>> n=eval(sys.argv[1])
>> except:
>> print ("needs integer as argument") ; exit()
>>
>> a=range(1,n+1)
>> b=np.array(a)
>> def func1(): return sum(a)
>> def func2(): return np.sum(b)
>>
>> print(f"sum with Python: {func1()} and NumPy {func2()} ")
>> tm1=it.timeit(stmt=func1, number=n)
>> print(f"time used Python Sum: {round(tm1,2)} sec")
>> tm2=it.timeit(stmt=func2, number=n)
>> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>>
>> and Here the C program:
>> #include
>> #include
>> #include
>> long func1(int n){
>> long r=0;
>> for (int i=1; i<= n;i++) r+= i;
>> return r;
>> }
>> int main(int argc, char* argv[]){
>> clock_t c0, c1;
>> long v,count; int n;
>>if ( argc < 2) {
>> printf("Please give an argument");
>> return -1;
>> }
>> n=atoi(argv[1]);
>> c0 = clock();
>> *for (int j=0;j < n;j++) v=func1(n);*
>> c1 = clock();
>> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
>> printf("\n\tThe value : %ld\n", v);
>> }
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>>
>
>
> --
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Re: [python-uk] C is it faster than numpy
Hi,
What do you mean?
the python and C programs are not equivalent?
Le ven. 25 févr. 2022 à 10:42, Giorgio Zoppi a
écrit :
> Well,
> numpy is written in C :) Maybe your C is not the numpy equivalent?
> Best Regards,
> Giorgio
>
> Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader <
> abdelkader.belahc...@enst.dz> ha scritto:
>
>> Hi,
>> a lot of people think that C (or C++) is faster than python, yes I agree,
>> but I think that's not the case with numpy, I believe numpy is faster than
>> C, at least in some cases.
>>
>>
>> *Is there another explanation ?Or where can find a doc speaking about
>> the subject?*Thanks a lot
>> Regards
>> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is
>> an example Let's look at the following case:
>> Here is the result on my laptop i3:
>>
>> Labs$ *python3 tempsExe.py 5*
>> sum with Python: 1250025000 and NumPy 1250025000
>> time used Python Sum: * 37.28 sec *
>> time used Numpy Sum: *1.85 sec*
>>
>> Labs$ *./tt5 *
>>
>>
>> * CPU time :7.521730The value : 1250025000 *
>>
>>
>> This is the Python3 program :
>>
>> import timeit as it
>> import numpy as np
>> import sys
>> try :
>> n=eval(sys.argv[1])
>> except:
>> print ("needs integer as argument") ; exit()
>>
>> a=range(1,n+1)
>> b=np.array(a)
>> def func1(): return sum(a)
>> def func2(): return np.sum(b)
>>
>> print(f"sum with Python: {func1()} and NumPy {func2()} ")
>> tm1=it.timeit(stmt=func1, number=n)
>> print(f"time used Python Sum: {round(tm1,2)} sec")
>> tm2=it.timeit(stmt=func2, number=n)
>> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>>
>> and Here the C program:
>> #include
>> #include
>> #include
>> long func1(int n){
>> long r=0;
>> for (int i=1; i<= n;i++) r+= i;
>> return r;
>> }
>> int main(int argc, char* argv[]){
>> clock_t c0, c1;
>> long v,count; int n;
>>if ( argc < 2) {
>> printf("Please give an argument");
>> return -1;
>> }
>> n=atoi(argv[1]);
>> c0 = clock();
>> *for (int j=0;j < n;j++) v=func1(n);*
>> c1 = clock();
>> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
>> printf("\n\tThe value : %ld\n", v);
>> }
>> ___
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>>
>
>
> --
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[python-uk] C is it faster than numpy
Hi,
a lot of people think that C (or C++) is faster than python, yes I agree,
but I think that's not the case with numpy, I believe numpy is faster than
C, at least in some cases.
*Is there another explanation ?Or where can find a doc speaking about the
subject?*Thanks a lot
Regards
Numpy implements vectorization for arrays, or I'm wrong. Anyway here is an
example Let's look at the following case:
Here is the result on my laptop i3:
Labs$ *python3 tempsExe.py 5*
sum with Python: 1250025000 and NumPy 1250025000
time used Python Sum: * 37.28 sec *
time used Numpy Sum: *1.85 sec*
Labs$ *./tt5 *
* CPU time :7.521730The value : 1250025000 *
This is the Python3 program :
import timeit as it
import numpy as np
import sys
try :
n=eval(sys.argv[1])
except:
print ("needs integer as argument") ; exit()
a=range(1,n+1)
b=np.array(a)
def func1(): return sum(a)
def func2(): return np.sum(b)
print(f"sum with Python: {func1()} and NumPy {func2()} ")
tm1=it.timeit(stmt=func1, number=n)
print(f"time used Python Sum: {round(tm1,2)} sec")
tm2=it.timeit(stmt=func2, number=n)
print(f"time used Numpy Sum: {round(tm2,2)} sec")
and Here the C program:
#include
#include
#include
long func1(int n){
long r=0;
for (int i=1; i<= n;i++) r+= i;
return r;
}
int main(int argc, char* argv[]){
clock_t c0, c1;
long v,count; int n;
if ( argc < 2) {
printf("Please give an argument");
return -1;
}
n=atoi(argv[1]);
c0 = clock();
*for (int j=0;j < n;j++) v=func1(n);*
c1 = clock();
printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
printf("\n\tThe value : %ld\n", v);
}
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