sh | preplish | |
---|---|---|
24 | 9 | |
6,862 | 4 | |
- | - | |
6.8 | 5.0 | |
about 2 months ago | 7 months ago | |
Python | Perl | |
MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sh
- sh: Python Process Launching
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Acme.sh runs arbitrary commands from a remote server
I usually replace shell scripts with python (using sh module: https://amoffat.github.io/sh/ for calling other scripts/programs).
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The Right Way to Run Shell Commands from Python
> sh relies on various Unix system calls and only works on Unix-like operating systems - Linux, macOS, BSDs etc. Specifically, Windows is not supported.
from: https://amoffat.github.io/sh/
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Anyone have any tips for developing on Windows?
You can even run interpreted languages as a shell. See plumbum or sh for ways to make it a more comfortable shell and ipython for a better version of the shell.
- Python “Sh” Module
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Argbash – Bash Argument Parsing Code Generator
100% agree. There are some libraries like https://amoffat.github.io/sh/ that aim to make that easier, but they always have some quirks that, funnily enough, are often the corner cases you were hitting in your complicated Bash script in the first place.
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Unix bash scripting versus Python - any resources out there for comparisons?
Another way to make Python scripts nicer is to use https://github.com/amoffat/sh
- Show HN: Clamshell- an experimental Python based shell
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Useful Python Modules for us
pdbpp: Improved pdb boltons: assorted python addtions twisted: event driven networking framework sorcery: Dark magic in python, things know where+how they are being called, helps reducing boilerplate sh: Better alternative for subprocess module, much more pythonic taskipy: npm run scipt_name like functionality snoop: pdb lite, record+replay function steps birdseye: graphical debugger remote-pdb: easy pdb from inside containers typer: wrapper around click for simpler code for CLIs arrow: Always TZ aware datetimes, plus more features more-itertools: more functions for iterators pydantic: data validation + dataclasses loguru: better logging notifiers: sending notifications from python
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What is your favorite ,most underrated 3rd party python module that made your programming 10 times more easier and less code ? so we can also try that out :-) .as a beginner , mine is pyinputplus
Sh sh and outside python, watch watch
preplish
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Interactive GCC (igcc) is a read-eval-print loop (REPL) for C/C++
> what's wrong with that?
Why nothing at all, of course. A REPL need not be more than a way to test and explore syntax, functions, and logical structures.
> the user experience is REPL-ish and it can help some people learn the _basics_ of the language
PREPLISH exists for Perl ^_^
https://github.com/viviparous/preplish
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online Perl editor
If this is for testing of syntax or of trivial code, it sounds like a good use-case for running a local REPL. (Example: https://github.com/viviparous/preplish)
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Not Your Grandfather’s Perl
This is a simple REPL project and the readme lists other Perl REPLs.
https://github.com/viviparous/preplish
Perl's concise syntax makes working in a REPL a pleasure. Python has a REPL but the design of the language makes it expand both in length (for loops) and in width (tabs).
I am a recent convert to working in a REPL first to test programming ideas.
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Has someone curated Perl data science resources somewhere? I've seen many such collections for other languages. Something like this, but with more modules and what they do:
I made this solution for some of my simple data wrangling: https://github.com/viviparous/preplish
- Is there any good reason not to use perl scripts in place of bash logic?
- Working with __DATA__ sections without Mojolicious
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Acme-ConspiracyTheory-Random
I tried the module it in a Perl REPL (https://github.com/viviparous/preplish) and got the following ravings that are worthy of a US loony politician:
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On Repl-Driven Programming
I agree with you that the immediate start-up and feedback is a great benefit to the coder. This is why I dislike complex, Rube-Goldbergian REPL systems.
There is a use-case for a throw-away interaction with a REPL. For example, how does $builtinFuncX work, or how would $data best be imported into a structure?
A REPL can also be a good initial approach to a more ambitious problem. In this case, a REPL can be good for focus and discipline.
If the second case is going to answer your concern and be constructive, it's necessary to be able to build the code for sharing and cleanly export the code for re-use.
I've had success tackling challenges using REPLs for Python and Perl [1] in both ways. But no tooling is going to solve the problem of a sloppy teammate who claims success just because "it compiles" and "it works on my box". A person who knows how to build good tooling goes further.
[1] https://github.com/viviparous/preplish
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Interactive C++ for Data Science
It is Jupyter is a Rube-Goldbergian nightmare. Python is a memory hog. There are better solutions, to be sure.
A simple REPL is all that's needed to both do A-type and B-type data exploration. (I won't use the term "data scientist", it's an exaggeration in most cases.)
Python has a REPL, R has a REPL, Perl has PDL and both a simple REPL (https://github.com/viviparous/preplish) and a more complex one (https://metacpan.org/pod/Reply).
Jupyter should not be used as an IDE because it is the wrong tool for development. A-type data explorers just want a painless UI and may not care much about the horrible agglutination of incomplete/slow/broken solutions that Jupyter represents.
What are some alternatives?
Delegator.py - Subprocesses for Humans 2.0.
xeus-cling - Jupyter kernel for the C++ programming language
envoy
tinyspec-cling - tiny spectral synthesizer with livecoding support
sarge
examples - Fully-working mlpack example programs
tkterminal - Terminal widget for Tkinter library.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
xonsh - :shell: Python-powered, cross-platform, Unix-gazing shell.
jupyter - An interface to communicate with Jupyter kernels.
zx - A tool for writing better scripts
slimux - SLIME inspired tmux integration plugin for Vim