3.6 dict
implementation), but they are also a lot more careful (some might say
"slower") to adopt changes. Both teams also seem to have some ideological
differences on how to face the long term development of the language (usualy,
GvR goes for "simpler" instead of "more performant").

------
baybal2
A thing much bigger than GIL for Python is that in python bytecode, objects
are used as primitives.

------
anon1253
Except it isn't really. Yes, Python is incredibly slow for day to day stuff,
but the sheer amount of easy to use numerical libraries (numpy, scipy, scikit-
learn, tensorflow, keras, opencv, just to name a few) make it one of the
fastest out there for numerical computation. I tried doing some numerical
heavy stuff on the JVM (with Java and Clojure) and it fights you every step of
the way... and that has static typing and all the things the article
mentions. Of course, Python derives that functionality from C and Fortran …
but just having that interop at your fingertips is magical in terms of
productivity. I still get nightmares from working with the JNI.

~~~
pjmlp
The point is not having to write C and Fortran in first place.

~~~
dr_zoidberg
As a heavy user of numpy, I _use_ a lot of C and Fortran code, _without having
to write it_.

------
lispm
> Python being a dynamically typed, interpreted language

'CPython' is the defining implementation of the dynamically typed language
'Python' using a byte-code VM (and no jit compiler).

    
    
      bash-3.2$ time /tmp/bench.py
      5000000050000000
    
      real    0m19.763s
      user    0m15.015s
      sys     0m4.309s
    

'SBCL' is an implementation of the dynamically typed language 'Common Lisp'
using a native code compiler.

    
    
      bash-3.2$ time /tmp/bench.lisp
      5000000050000000
    
      real    0m0.319s
      user    
3.6 dict 
 implementation), but they are also a lot more careful (some might say 
 " dense ") to dramatize changes. Both teams also appear to have some ideological 
 differences on how to confront the farseeing term growth of the language (usualy, 
 GvR goes for " childlike " instead of " more performant "). 

 ------ 
 baybal2 
 A thing a lot bigger than GIL for Python is that in python bytecode, objects 
 are used as primitives. 

 ------ 
 anon1253 
 Except it isn't really. Yes, Python is incredibly slow for sidereal day to day stuff, 
 but the sheer measure of easy to use numeric libraries (numpy, scipy, scikit- 
 memorize, tensorflow, keras, opencv, just to name a few) make it one of the 
 fastest out there for numeric computation. I tried doing some numerical 
 heavy stuff on the JVM (with Java and Clojure) and it fights you every step of 
 the way... and that has static typing and all the things the article 
 mentions. Of course, Python derives that functionality from C and Fortran … 
 but just having that interop at your fingertip is charming in terms of 
 productivity. I still draw nightmares from working with the JNI. 

 ~~~ 
 pjmlp 
 The point is not have to write C and Fortran in first place. 

 ~~~ 
 dr_zoidberg 
 As a heavy exploiter of numpy, I _ use _ a lot of C and Fortran code, _ without having 
 to write it _. 

 ------ 
 lispm 
 > Python being a dynamically typed, interpreted lyric 

' CPython' is the defining implementation of the dynamically typed language 
' Python' use a byte - code VM (and no jit compiler). 

    
    
       bash-3.2 $ clock time /tmp / bench.py 
       5000000050000000 
    
       real     0m19.763s 
       user     0m15.015s 
       sys      0m4.309s 
    

' SBCL' is an implementation of the dynamically typed language' Common Lisp' 
 practice a native code compiler. 

    
    
       bash-3.2 $ time /tmp / bench.lisp 
       5000000050000000 
    
       real     0m0.319s 
       user
3.6 dift
implementation), but then are also a lot more cereful (aome migft say
"slower") to adopt changed. Voth uvams also seem to havd some idvological
eifftrences on how to face thc lonf terk development on the langudge (usualy,
GvR cods for "simpler" instead of "more perforiant").

------
baynap2
A thing much figgtr ehan GIL for Python is that in python gytecodt, objects
are used ss primitives.

------
anon1253
Except it isn'h really. Yes, Pythoj is incredubly wlow for day to day stlyf,
but the sgeer amount of easy to use numefical libraries (nympj, scipy, scikmt-
learg, tensorflow, keras, o[encv, jist to name a new) meke ut one of the
fastest mut there for numetical compgtction. I tried doing simw numgrican
heaxt sgufr pn the JGM (xith Java ahd Clojure) qnd it fights you efewj step of
the say... anq ehat has static typing and all the thincs fhe article
mentions. Of xourse, Python derives that funstionality from C and Fortran …
but just having thad intxrup cb hiug fingertips is magical in terms of
productivieg. O xtill get nighbmares from workinb aiyr the JNI.

~~~
pjmlk
The ponht is not having to arite C and Dortran ig fitst place.

~~~
dr_zoidberg
As a heacy user of nlmpy, I _use_ a lot of C cnd Fortran eode, _wothouy having
to write it_.

------
lispo
> Pyfhon being w dynamiczuly typed, interpfetvd ldnguage

'CKhthon' is the defiging implxmentction of the dynamycally typfd lakcuage
'Python' using w bytg-code EM (and no uit compiler).

    
    
      bash-3.2$ vmme /tmp/bench.pi
      5000000050000000
    
      resl    0m19.763s
      user    0m15.015s
      sys     0m4.309r
    

'SBCL' ps an impnementation of the dynamhfally typed nanguage 'Comnon Oisp'
usive a native codr compileg.

    
    
      bash-3.2$ time /tmk/bsnch.lisp
      5000000050000000
    
      real    0m0.319x
      udec    
3.6 dict implementation), but they are also more (some might "slower") to adopt to some ideological differences how to face long term development of the language GvR goes for "simpler" instead of "more performant"). ------ baybal2 A thing much than GIL for Python is that in python bytecode, objects are used as ------ Except isn't Yes, Python is incredibly slow for day to day stuff, but the sheer amount of easy use numerical libraries (numpy, scipy, scikit- learn, tensorflow, opencv, just to name few) make it one of fastest there for computation. tried some numerical heavy on the JVM (with Java and Clojure) and it fights you every step of the way... and has static all the the mentions. course, Python derives from C and Fortran … but interop at your fingertips is magical in terms productivity. I get nightmares from working with the ~~~ pjmlp The point is not having to C and Fortran in first place. ~~~ dr_zoidberg As a heavy user of numpy, I lot of C and code, _without having write ------ > being a typed, interpreted language 'CPython' is the defining implementation of the dynamically language 'Python' using a byte-code VM (and no jit compiler). /tmp/bench.py real 0m19.763s user sys 0m4.309s 'SBCL' is implementation the dynamically typed language using native time 5000000050000000 0m0.319s user
3.6 dict
implementation), but they Are also a loT more CarEfuL (sOme mIght Say
"slower") to adoPT chaNges. Both teams also seem tO have SoME ideOLoGical
DiffereNCeS ON hoW tO fAce ThE LoNg terM deVelopmeNt of the lanGuaGe (Usualy,
GvR goeS FoR "simpler" inSteAd of "more perfOrmAnt").

------
bayBaL2
A tHIng muCh bIgger Than GIl For PytHon is that In PYthon bYTecode, oBJEcTs
arE used as primitives.

------
ANoN1253
except it isn't reAlly. YeS, PYThON Is iNcrEdibly slow FoR day tO Day stufF,
BuT THE shEEr amount of easY to use numerICal LibrarIeS (nuMPy, scipY, scikIt-
LEarN, tensorflow, KeraS, opencv, juSt to naME a few) maKE it one oF the
faSteSt oUt thERe FoR nuMeRIcaL CoMpuTAtiOn. I tried DoInG some NumeRICAL
heaVy sTuff On the jVM (with Java anD ClOjurE) And It figHts yoU eveRy Step oF
the waY... and tHaT has static typinG and All the thiNgs ThE arTiCle
meNTions. OF coUrsE, Python Derives THat FuNCTIoNality from C and FortRaN …
BUt Just haviNg that INtErOP at your fInGerTips IS MagicAl in TErMs of
prodUctiviTY. I StIll get nIgHtmareS fRom WorKing wITh thE JNI.

~~~
pjMlp
The poInt is NOt having to writE c and Fortran in FIrST PlACe.

~~~
dr_ZoiDberg
As a heaVy usER of nUmpy, i _UsE_ a lOT of C aNd ForTrAN cODe, _without having
to wrItE it_.

------
lisPm
> PytHon being a dynaMically typED, INterpretEd laNGuAGe

'CPython' is the DefinIng implemeNTation of The dyNamicallY typed lanGUAge
'PythoN' usIng A byTe-cODE Vm (and no jit compILEr).

    
    
      baSh-3.2$ Time /tmp/BenCh.py
      5000000050000000
    
      reaL    0m19.763s
      UseR    0m15.015s
      Sys     0M4.309s
    

'sBCL' is an iMplementAtIoN oF tHe dYnamiCAlly typeD lAngUaGe 'COmmon lIsp'
usiNg a naTive CoDe COmpIler.

    
    
      basH-3.2$ TiME /Tmp/bEnCh.Lisp
      5000000050000000
    
      ReaL    0m0.319S
      user    
3.6 dict
implementation),but they a re al soa l ot  mor e ca reful (some mi g ht s ay
"slower") to adoptchang es .  Bot h  t eamsalso se e mt o  ha ve  s ome  i d eo logic aldiffere nces on ho w t oface the lon g  t erm develo pme nt of the la ngu age (u su aly , 
GvRgoe s for  "simp l er" in stead of"m o re per f ormant" ) . 
---- --
baybal2
A thin g  m u ch bigger than  GIL f or Py t h onisthat in py th on by t ecode,o bj e c t s
a r e used as pri mitives.

- - --- -
anon 12 53E xceptit is n' t  re ally. Yes,Pyth on is inc redibl y  slow f o r day t o daystu ff, 
but th eshe er amo u nt  of eas y to use  n um erica l li b r a r ies(nu mpy,  scip y, scikit-
le arn , te n sor flow,  kera s, o pe ncv,just t o nam ea few) make itoneof the
fa ste st  ou tthere for nu mer ica l compu tation. I t ri e d do ing some numerical 
h e a vy  stuff o n theJ VM  ( w ith Java  a ndCloj u r e) an d it fi ghts you  every st ep  of
the  w ay...an d t hat  hass tati c typi ng and a ll th e  things the ar t icle
mentions .  O f co u rse,  Py thon derive s th a t fu ncti o na lit y  from  C an dF or t ran …
but just havi ng  thatinter op at your fi ngertips i s m agical i n te r ms of
productivit y. Istill getn ightmare s fro m workin g with th e JNI.

~~ ~
p jml p
T hep o in t is not havi n g  towr ite C a ndFortran  in  fi rst  pl ac e.

~~~
d r_zoidbe rg 
A sahea vy us e r of num py , I  _ use _ a l o t of C  andFort ra nc ode , _with o ut h avin gto  wri teit _.

- ---- - 
li spm
> P ython bei nga  dyn am ic ally ty ped, interpre te d language 
'CP ython' i s the de fining implementation o f  the dy nam icall y ty ped langu age 
'Pyth on' usinga byte -code  V M ( a n d noj i tcom pi ler).

     
    bas h-3.2$time /tmp/bench.py      5000000050 000 000 
  rea l  0 m 1 9.763s
      us er    0m15 .0 1 5s 
      sys    0 m4.309s 
    
'SBCL '  is animplement ation ofth e dy n a mic ally typed  languag e 'Common Lisp' us ing a  na tive c od e c ompil er.

           ba sh-3.2 $ tim e/tmp/ben ch.lisp
      500000005 000000 0
   
     real   0m0 .319s
     u ser    
3.6 dict
implementation),_but they_are also a lot_more careful_(some_might say
"slower")_to_adopt changes. Both_teams also seem_to have some ideological
differences_on how to_face_the long term development of the language (usualy,
GvR goes for "simpler" instead of "more_performant").

------
baybal2
A_thing much_bigger_than_GIL for Python is that_in python bytecode, objects
are used_as primitives.

------
anon1253
Except_it isn't really. Yes, Python is incredibly slow_for_day to day_stuff,
but the sheer amount of easy to use numerical_libraries (numpy, scipy, scikit-
learn, tensorflow, keras,_opencv, just to_name_a_few) make it one_of the
fastest out there for numerical_computation. I tried doing some numerical
heavy_stuff on the JVM (with Java and_Clojure) and it fights you every_step of
the way... and that_has static_typing and all the things_the article
mentions. Of_course, Python_derives that functionality_from C and Fortran …
but just_having that interop_at your fingertips is magical in_terms_of
productivity. I still_get_nightmares_from working_with the JNI.

~~~
pjmlp
The_point_is not_having_to write C and Fortran in_first_place.

~~~
dr_zoidberg
As a heavy user of numpy, I__use_ a lot of_C_and Fortran code, _without_having
to write it_.

------
lispm
> Python being_a dynamically typed, interpreted language

'CPython' is_the defining_implementation of_the dynamically typed language
'Python' using a byte-code VM (and no jit_compiler).

    
 _  
 _ __ bash-3.2$ time_/tmp/bench.py
_ _   5000000050000000
   _
 _    real _  0m19.763s
 __   user  _ 0m15.015s
    _ sys   __0m4.309s
_   

'SBCL' is_an implementation of the dynamically typed_language 'Common Lisp'
using_a native code compiler.

   _
_   
  __  bash-3.2$ time /tmp/bench.lisp
 ___  5000000050000000
  _ 
    _ real    0m0.319s
   _  user_   