Live data from Hacker News

Economics Nobel laureate Paul Romer is a Python programming convert

qz.com

21–30 of 74 posts

Re: Economics Nobel laureate Paul Romer is a Python programming convert

#21
post #14

Beyond the cool python angle, I find the following statement quite interesting: "...James Somers argued that Jupyter notebooks may replace the traditional research paper typically shared as a PDF..." I've only been exposed to jupyter notebook references here and there...but i guess i should become a little more familiar with them.

I was recently asked by a company to do a take-home assignment, some data analysis stuff. I opted for a jupyter notebook as it sequentially allows you to both explore the data in an interactive manner while still programming in Python. It was great. I love notebooks.

Re: Economics Nobel laureate Paul Romer is a Python programming convert

#23
post #14

Beyond the cool python angle, I find the following statement quite interesting: "...James Somers argued that Jupyter notebooks may replace the traditional research paper typically shared as a PDF..." I've only been exposed to jupyter notebook references here and there...but i guess i should become a little more familiar with them.

While I think this is a good thing, an additional requirement is needed. The published notebook should be sequentially executed by an automated tool. Users can execute commands out of order and have artifacts from deleted commands. Without verification, you can be publishing notebooks with bugs in them that you don't see until you re-execute them.

Re: Economics Nobel laureate Paul Romer is a Python programming convert

#24
post #23
post #14

Beyond the cool python angle, I find the following statement quite interesting: "...James Somers argued that Jupyter notebooks may replace the traditional research paper typically shared as a PDF..." I've only been exposed to jupyter notebook references here and there...but i guess i should become a little more familiar with them.

While I think this is a good thing, an additional requirement is needed. The published notebook should be sequentially executed by an automated tool. Users can execute commands out of order and have artifacts from deleted commands. Without verification, you can be publishing notebooks with bugs in them that you don't see until you re-execute them.

Jupyter notebooks are ultimately just a text file, which can be version controlled, verified (via the VCS or independent hashing), and if necessary - digitally signed in a cryptographically secure manner.

Given that Git is already secure, I'd say all the researcher needs to know is basic usage of Git. Version controlling the notebook satisfies all the requirements you mentioned: prevents accidental distortion, is verifiable, restorable, etc.

Re: Economics Nobel laureate Paul Romer is a Python programming convert

#25
I use Python and it's OK, but I find a lot of the breathless hype around it as a language a little baffling as there are more than an average number of stupidities in there. However this piece is really around exploiting the amazing infrastructure that has built up around Python that empowers mathematical and statistical research, which is fair enough.

Re: Economics Nobel laureate Paul Romer is a Python programming convert

#26
post #7

> he tried to use Mathematica to share one of his studies in a way that anyone could explore every detail of his data and methods. It didn’t work. He says that Mathematica’s owner, Wolfram Research, made it too difficult to share his work in a way that didn’t require other people to use the proprietary software, too Sometimes I wonder where Mathematica would be if it were open sourced lets say in 2010. It had such a…

We could recover Mathematica interop easily. i wrote a tool to convert Python expressions to Mathematica expressions, evaluate them, and return the results as Python expressions, probably back in 97. I believe M still has the Kernel C API so it should be easy to resurrect & make modern. http://library.wolfram.com/infocenter/MathSource/585/

Re: Economics Nobel laureate Paul Romer is a Python programming convert

#29

I use Python and it's OK, but I find a lot of the breathless hype around it as a language a little baffling as there are more than an average number of stupidities in there. However this piece is really around exploiting the amazing infrastructure that has built up around Python that empowers mathematical and statistical research, which is fair enough.

What are these "stupidities" that bother you?

Personally, I use Python a lot for anything related to data science. No language is perfect, but I'd say Python has a lot less deficiencies and warts than most other mainstream languages, and it's extremely well suited for tasks in data science and related fields such as machine learning.

The main issue with Python is that its default platform (CPython) isn't very efficient. That's not a problem in the language itself; in fact, it's partly caused by all the benefits of a high-level language: you simply don't have the same facilities to optimize your resource consumption as you do in, say, Rust.

The upside is of course that the code is far more concise and readable.

Re: Economics Nobel laureate Paul Romer is a Python programming convert

#30
post #29

I use Python and it's OK, but I find a lot of the breathless hype around it as a language a little baffling as there are more than an average number of stupidities in there. However this piece is really around exploiting the amazing infrastructure that has built up around Python that empowers mathematical and statistical research, which is fair enough.

What are these "stupidities" that bother you? Personally, I use Python a lot for anything related to data science. No language is perfect, but I'd say Python has a lot less deficiencies and warts than most other mainstream languages, and it's extremely well suited for tasks in data science and related fields such as machine learning. The main issue with Python is that its default platform (CPython) isn't very efficie…

Whilst that is true, for data science related things I end up using Pandas (and Numpy) often which are fast.

So most of the heavy lifting is not done by python itself.

Post reply on HN