Data Science at the Command Line
datascienceatthecommandline.com
Data Science at the Command Line
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Re: Data Science at the Command Line
#2As a developer I have not paid much attention to the power of command line tools, to get things done. This book is a great resource that uses docker to create simple command line utilities to implement useful Data Science functionality.
Re: Data Science at the Command Line
#3Believe it or not, it's partly thanks to you, HN, that I wrote this book in 2014 in the first place [1]! It's humbling to see it listed here again now that the text has become available under a CC BY-ND license. Thank you for your help in spreading the word.
All this attention (read: likes, shares, and page views) is making me wonder whether it's worthwhile to write an update (or even a second edition). What do you think? What would you like to see changed or added?
Re: Data Science at the Command Line
#4While the efficiency of a command line is always sexy, but for data science in particular, where reproducibility is important and bugs are subtle and often don't cause a terminal error, it is worth it to sacrifice a little bit of code efficiency for code clarity in the long run by using an IDE/Notebook.
Re: Data Science at the Command Line
#5While the efficiency of a command line is always sexy, but for data science in particular , where reproducibility is important and bugs are subtle and often don't cause a terminal error, it is worth it to sacrifice a little bit of code efficiency for code clarity in the long run by using an IDE/Notebook.
Is there a tool that would create a notebook from the command-line?
Re: Data Science at the Command Line
#6While the efficiency of a command line is always sexy, but for data science in particular , where reproducibility is important and bugs are subtle and often don't cause a terminal error, it is worth it to sacrifice a little bit of code efficiency for code clarity in the long run by using an IDE/Notebook.
Is there a tool that would create a notebook from the command-line?
Jupyter notebook has Bash kernels
Re: Data Science at the Command Line
#7While the efficiency of a command line is always sexy, but for data science in particular , where reproducibility is important and bugs are subtle and often don't cause a terminal error, it is worth it to sacrifice a little bit of code efficiency for code clarity in the long run by using an IDE/Notebook.
Is there a tool that would create a notebook from the command-line?
I think that depends on what you mean;
Jupyter lets you run magic[0] commands (run bash commands in subprocesses), and there's also a bash kernel[1].
[0]: https://blog.dominodatalab.com/lesser-known-ways-of-using-no...
Re: Data Science at the Command Line
#8Earlier quoted context omitted.
Is there a tool that would create a notebook from the command-line?
Jupyter notebook has Bash kernels
Re: Data Science at the Command Line
#9While the efficiency of a command line is always sexy, but for data science in particular , where reproducibility is important and bugs are subtle and often don't cause a terminal error, it is worth it to sacrifice a little bit of code efficiency for code clarity in the long run by using an IDE/Notebook.
Notebooks are not much better than copy-pasting from a notepad or editor into an interpreter. They’re great for reports, but dangerous for presenting the illusion of reproducibility.
At best you’re constantly restarting your kernel and clearing output. More likely, output from cell #7 has modified output [138] but you haven’t updated the chart produced in cell #17 (or some similar craziness). Not much better than programming with GOTOs.
“But they’re great for reporting and visualization!” you might say. If you’re building any report of value, though, it will influence important decisions. That’s the reason your code shouldn't live in a notebook. It should be in a library, covered by unit tests, so that those decisions aren’t based on faulty logic.