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> I vehemently disagree. Readability is the most important consideration. It's "subjective", but most human "subjects" prefer Python to Perl on this dimension,

by _Mask_ 5y ago

> I vehemently disagree. Readability is the most important consideration. It's "subjective", but most human "subjects" prefer Python to Perl on this dimension, and it's very important to them. That's why we are where we are today. I absolutely agree that readability is most important consideration. I just disagree that it depends on the language. As said before, there is a lot of write-only code written by data scientists in Python. > Well sorry, but I don't want to get used to your favorite language's line noise. That's why I choose another language. That's why everyone else chooses another language. You don't have to. It's called a programming language paradigm, not noise. You either learn it or not when studying a language. If you want to understand something without studying it, it's your choice. I am proficient in both of these languages and can see the strengths and weaknesses in both of them. > Those libraries were developed in Python because the authors of those libraries preferred to work in Python. The authors preferred to work in Python because of the legibility considerations I've already mentioned. Perl was extensively used by scientific community, but Python was the lucky one that was backed by Google in 2005. And that's ok. It is better than Perl for data science now, but not because of its syntax :)

civilized 5y ago
We'll have to agree to disagree on most of this, which is fine, better than having a stupidity-driven argument. I'm sure the aspects of Perl that are weird and gross to me are great for some other people. > Perl was extensively used by scientific community, but Python was the lucky one that was backed by Google in 2005. I'm curious about this. Can you point to any specific evidence that being "backed by Google in 2005" was a momentous event for Python? AFAIK Google is not a major player in the Python scientific computing / data analysis / ML community outside of TensorFlow, which was introduced after Python was already huge.
- _Mask_ 5y ago
  > I'm curious about this. Can you point to any specific evidence that being "backed by Google in 2005" was a momentous event for Python? Here are some stats with the popularity of various languages over time: https://statisticsanddata.org/data/the-most-popular-programming-languages-1965-2021/ https://statisticsanddata.org/data/the-most-popular-programm... In 2005 Google hired Guido van Rossum, where he spent half of his time developing the Python language. As you can see, before 2006, Python was little less popular than Perl(9th vs 8th). By the time Guido left Google(December 2012), Python became 4th most popular programming language.
  - civilized 5y ago
    I don't think Guido did anything special for the scientific/technical/data computing community after he came to Google. Python was on version 2.4 as of 2005, and that version would have been fine to develop against indefinitely. In fact, it's been a huge headache getting scientists to move to Python 3.
  - teleforce 5y ago
    The sibling comment mentioned that there's probably no contribution from Guido regarding the meteoric rise of Python in data science but the truth it's really the opposite [1]. Guido was really instrumental in suitability of Python for data science [2]. To quote from the magazine article on Python for Scientists and Engineers: "During these early years, there was considerable interaction between the standard and scientific Python communities. In fact, Guido van Rossum, Python's Benevolent Dictator For Life (BDFL), was an active member of the matrix-sig. This close interaction resulted in Python gaining new features and syntax specifically needed by the scientific Python community. While there were miscellaneous changes, such as the addition of complex numbers, many changes focused on providing a more succinct and easier to read syntax for array manipulation. For instance, the parenthesis around tuples were made optional so that array elements could be accessed through, for example, a[0,1] instead of a[(0,1)]. The slice syntax gained a step argument— a[::2] instead of just a[:], for example—and an ellipsis operator, which is useful when dealing with multidimensional data structures." Personally I'd much prefer D language to replace python for data science due to its Python like programming vibes but with more powerful real-time programming constructs, and its suitability for beginners or type A data scientists [3]. Python is already showing its limitations especially for the B type of data scientists [4]. There's recent discussions on Dlang forum for the potential improvements of D for data science and there's also recommendation for Walter to join such SIG as was Guido [5]. [1]Numpy History: https://en.wikipedia.org/wiki/NumPy#History https://en.wikipedia.org/wiki/NumPy#History There's even recomendation from Dlang forum for Walter to join such SIG [3]. [2]Python for Scientists and Engineers: https://www.computer.org/csdl/magazine/cs/2011/02/mcs2011020009/13rRUx0xPMx https://www.computer.org/csdl/magazine/cs/2011/02/mcs2011020... [3]Teaching D from Scratch: Is it a viable first language? https://news.ycombinator.com/item?id=29711800 https://news.ycombinator.com/item?id=29711800 [4]Type A Data Scientist vs. Type B Data Scientist: https://www.projectpro.io/article/type-a-data-scientist-vs-type-b-data-scientist/194 https://www.projectpro.io/article/type-a-data-scientist-vs-t... [5]Dlang forum: Pandas like features: https://forum.dlang.org/thread/zuebxhkycgxsoipsgnbk@forum.dlang.org https://forum.dlang.org/thread/zuebxhkycgxsoipsgnbk@forum.dl...

by _Mask_ 5y ago

civilized 5y ago

We'll have to agree to disagree on most of this, which is fine, better than having a stupidity-driven argument. I'm sure the aspects of Perl that are weird and gross to me are great for some other people. > Perl was extensively used by scientific community, but Python was the lucky one that was backed by Google in 2005. I'm curious about this. Can you point to any specific evidence that being "backed by Google in 2005" was a momentous event for Python? AFAIK Google is not a major player in the Python scientific computing / data analysis / ML community outside of TensorFlow, which was introduced after Python was already huge.

_Mask_ 5y ago
> I'm curious about this. Can you point to any specific evidence that being "backed by Google in 2005" was a momentous event for Python? Here are some stats with the popularity of various languages over time: https://statisticsanddata.org/data/the-most-popular-programming-languages-1965-2021/ https://statisticsanddata.org/data/the-most-popular-programm... In 2005 Google hired Guido van Rossum, where he spent half of his time developing the Python language. As you can see, before 2006, Python was little less popular than Perl(9th vs 8th). By the time Guido left Google(December 2012), Python became 4th most popular programming language.
- civilized 5y ago
  I don't think Guido did anything special for the scientific/technical/data computing community after he came to Google. Python was on version 2.4 as of 2005, and that version would have been fine to develop against indefinitely. In fact, it's been a huge headache getting scientists to move to Python 3.
- teleforce 5y ago
  The sibling comment mentioned that there's probably no contribution from Guido regarding the meteoric rise of Python in data science but the truth it's really the opposite [1]. Guido was really instrumental in suitability of Python for data science [2]. To quote from the magazine article on Python for Scientists and Engineers: "During these early years, there was considerable interaction between the standard and scientific Python communities. In fact, Guido van Rossum, Python's Benevolent Dictator For Life (BDFL), was an active member of the matrix-sig. This close interaction resulted in Python gaining new features and syntax specifically needed by the scientific Python community. While there were miscellaneous changes, such as the addition of complex numbers, many changes focused on providing a more succinct and easier to read syntax for array manipulation. For instance, the parenthesis around tuples were made optional so that array elements could be accessed through, for example, a[0,1] instead of a[(0,1)]. The slice syntax gained a step argument— a[::2] instead of just a[:], for example—and an ellipsis operator, which is useful when dealing with multidimensional data structures." Personally I'd much prefer D language to replace python for data science due to its Python like programming vibes but with more powerful real-time programming constructs, and its suitability for beginners or type A data scientists [3]. Python is already showing its limitations especially for the B type of data scientists [4]. There's recent discussions on Dlang forum for the potential improvements of D for data science and there's also recommendation for Walter to join such SIG as was Guido [5]. [1]Numpy History: https://en.wikipedia.org/wiki/NumPy#History https://en.wikipedia.org/wiki/NumPy#History There's even recomendation from Dlang forum for Walter to join such SIG [3]. [2]Python for Scientists and Engineers: https://www.computer.org/csdl/magazine/cs/2011/02/mcs2011020009/13rRUx0xPMx https://www.computer.org/csdl/magazine/cs/2011/02/mcs2011020... [3]Teaching D from Scratch: Is it a viable first language? https://news.ycombinator.com/item?id=29711800 https://news.ycombinator.com/item?id=29711800 [4]Type A Data Scientist vs. Type B Data Scientist: https://www.projectpro.io/article/type-a-data-scientist-vs-type-b-data-scientist/194 https://www.projectpro.io/article/type-a-data-scientist-vs-t... [5]Dlang forum: Pandas like features: https://forum.dlang.org/thread/zuebxhkycgxsoipsgnbk@forum.dlang.org https://forum.dlang.org/thread/zuebxhkycgxsoipsgnbk@forum.dl...