4 ms·

I think it’s about 95% the academic thing. Not sure exactly how Python came into favor as the preferred scripting language of Academia, but once it did that bas

I think it’s about 95% the academic thing. Not sure exactly how Python came into favor as the preferred scripting language of Academia, but once it did that basically ensured the math and science ecosystem would be very robust, which in turn made Python an obvious good choice for data science, ML, etc.

jbverschoor 6y ago
Well, python was created by a Dutch guy who worked at the research center for math, later some other research positions. the CWI has ties with the university, so that makes sense. Python is older than ruby, and matz probably had less ties, and probably uses more JP than guido did Dutch.
- tinco 6y ago
  Yeah even if Ruby is not much younger than Python, it only gained popularity outside of Japan somewhere around 2003, and only extreme popularity after the release of rails.
  - jbverschoor 6y ago
    I always wondered how dhh came into contact with this unknown language
    - tinco 6y ago
      It was not that unknown, there were definitely people using it in the west before rails was released. It was just not in the mainstream consciousness.
    - jhbadger 6y ago
      I was using Ruby regularly circa 2001. It wasn't unknown in the West, as various magazines had pointed out this neat Smalltalk-like scripting language and the first version of the Pickaxe book was also published in 2001.
kragen 6y ago
I think what happened was that Jim Hugunin wrote Numeric (Numerical Python) in 1995 with help from a bunch of other people, and Travis Oliphant and some other people put together SciPy in 2001 from modules they'd been working on previously. A hecking lot of work had already gone into Numeric and SciPy by the time I first tried using it, around 2003, and so it was already pretty stable, with a broad range of capabilities. It had already left Perl's PDL far behind (except for 3-D plotting support), and Ruby's NArray barely existed, being under 10KLOC when it was imported from CVS in 2001: https://github.com/masa16/narray/commits/master?after=447c960c5d475fa898224e20235d585709739a50+139 https://github.com/masa16/narray/commits/master?after=447c96... So Numeric and then SciPy (2001), numarray (2001), and Numpy (2005) just had a big head start on the other free-software numerical computation libraries, basically because a few early Pythonistas were interested in the field, and it kept accruing more and more powerful stuff: FFTW (1998), SparsePy (1999), the ability to read MATLAB files (1999), matplotlib (2001), IPython (2001), and so on. It got popular enough that dozens of people started contributing code to it, making it grow faster and faster. At the time, most scientists were still using MATLAB; other alternatives for array-based computing included R, Bell Labs's Lush, and if you were a real masochist, PV-WAVE's IDL. Octave, the free-software MATLAB clone, wasn't yet a reasonable replacement. And all of these languages had the disadvantage that they were sort of trapped in their own little library ecosystems: if you wanted to do Gaussian quadrature or calculate a Kullback-Leibler divergence or plot some data, you were golden, but if you wanted to scrape some data out of some web pages with regular expressions, parse command-line flags, read members of a zip file, or run as a CGI process on a web server, you were shit out of luck. Numpy's syntax for a vector of numbers is pretty shitty compared to Octave or APL, but it's worlds better than R or especially IDL, and most people prefer it to Lush's Lisp syntax, although I have a soft spot for Lush myself. And Python and Numpy have reasonable language semantics too, unlike Octave or R. (I think Lush may have been a bit late to the free-software party, too, so it wasn't really an option.) So academia started switching over from MATLAB to Numpy, I think around 2010. The transition isn't over yet; the numerical-methods class I audited last year is still using Octave. R is dominant in statistics and will probably stay that way. But Numpy is pretty broadly adopted. It basically came down to a head start due to a lot of hard work done in the 1990s that just happened to be done in Python. It's maybe not entirely coincidence that the people whose taste ran to reimplementing Gaussian quadrature were fans of Python and not Perl, Ruby, Lua, or Tcl, but they could have been.

jbverschoor 6y ago

Well, python was created by a Dutch guy who worked at the research center for math, later some other research positions. the CWI has ties with the university, so that makes sense. Python is older than ruby, and matz probably had less ties, and probably uses more JP than guido did Dutch.

tinco 6y ago
Yeah even if Ruby is not much younger than Python, it only gained popularity outside of Japan somewhere around 2003, and only extreme popularity after the release of rails.
- jbverschoor 6y ago
  I always wondered how dhh came into contact with this unknown language
  - tinco 6y ago
    It was not that unknown, there were definitely people using it in the west before rails was released. It was just not in the mainstream consciousness.
  - jhbadger 6y ago
    I was using Ruby regularly circa 2001. It wasn't unknown in the West, as various magazines had pointed out this neat Smalltalk-like scripting language and the first version of the Pickaxe book was also published in 2001.

kragen 6y ago

I think what happened was that Jim Hugunin wrote Numeric (Numerical Python) in 1995 with help from a bunch of other people, and Travis Oliphant and some other people put together SciPy in 2001 from modules they'd been working on previously. A hecking lot of work had already gone into Numeric and SciPy by the time I first tried using it, around 2003, and so it was already pretty stable, with a broad range of capabilities. It had already left Perl's PDL far behind (except for 3-D plotting support), and Ruby's NArray barely existed, being under 10KLOC when it was imported from CVS in 2001: https://github.com/masa16/narray/commits/master?after=447c960c5d475fa898224e20235d585709739a50+139 https://github.com/masa16/narray/commits/master?after=447c96... So Numeric and then SciPy (2001), numarray (2001), and Numpy (2005) just had a big head start on the other free-software numerical computation libraries, basically because a few early Pythonistas were interested in the field, and it kept accruing more and more powerful stuff: FFTW (1998), SparsePy (1999), the ability to read MATLAB files (1999), matplotlib (2001), IPython (2001), and so on. It got popular enough that dozens of people started contributing code to it, making it grow faster and faster. At the time, most scientists were still using MATLAB; other alternatives for array-based computing included R, Bell Labs's Lush, and if you were a real masochist, PV-WAVE's IDL. Octave, the free-software MATLAB clone, wasn't yet a reasonable replacement. And all of these languages had the disadvantage that they were sort of trapped in their own little library ecosystems: if you wanted to do Gaussian quadrature or calculate a Kullback-Leibler divergence or plot some data, you were golden, but if you wanted to scrape some data out of some web pages with regular expressions, parse command-line flags, read members of a zip file, or run as a CGI process on a web server, you were shit out of luck. Numpy's syntax for a vector of numbers is pretty shitty compared to Octave or APL, but it's worlds better than R or especially IDL, and most people prefer it to Lush's Lisp syntax, although I have a soft spot for Lush myself. And Python and Numpy have reasonable language semantics too, unlike Octave or R. (I think Lush may have been a bit late to the free-software party, too, so it wasn't really an option.) So academia started switching over from MATLAB to Numpy, I think around 2010. The transition isn't over yet; the numerical-methods class I audited last year is still using Octave. R is dominant in statistics and will probably stay that way. But Numpy is pretty broadly adopted. It basically came down to a head start due to a lot of hard work done in the 1990s that just happened to be done in Python. It's maybe not entirely coincidence that the people whose taste ran to reimplementing Gaussian quadrature were fans of Python and not Perl, Ruby, Lua, or Tcl, but they could have been.