mercury
pytudes
mercury | pytudes | |
---|---|---|
77 | 100 | |
3,779 | 22,397 | |
1.0% | - | |
8.5 | 8.3 | |
16 days ago | 12 days ago | |
Python | Jupyter Notebook | |
GNU Affero General Public License v3.0 | MIT License |
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.
mercury
-
Ask HN: What's the best charting library for customer-facing dashboards?
I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.
My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/
disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury
-
mercury VS solara - a user suggested alternative
2 projects | 13 Oct 2023
-
Show HN: Web App with GUI for AutoML on Tabular Data
Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
-
streamlit VS mercury - a user suggested alternative
2 projects | 8 Jul 2023
- GitHub - mljar/mercury: Convert Jupyter Notebooks to Web Apps
-
[P] Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
The GitHub repository https://github.com/mljar/mercury
-
Show HN: Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.
It is very opinionated:
- it has no callbacks - we automatically re-execute cells below updated widget
- it has no layout widgets, all input widgets are always in the left sidebar
Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.
The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.
The GitHub repository https://github.com/mljar/mercury
Documentation https://RunMercury.com/docs/
Mercury Cloud https://cloud.runmercury.com
- Show HN: Build Web Apps in Jupyter Notebook with Python Only
-
[OC] Analyzing 15,963 Job Listings to Uncover the Top Skills for Data Analysts (update)
Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.
-
[OC] Data Analyst Skills in need based on 15,963 job listings
Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.
pytudes
-
Ask HN: High quality Python scripts or small libraries to learn from
Peter Norvig's work is great to learn from https://github.com/norvig/pytudes
- Norvig's 2023 Advent of Code
- Ask HN: How to build mastery in Python?
- SQL for Data Scientists in 100 Queries
- Bicycling Statistics
-
Ask HN: How to deal with the short vs. long function argument
I've been a programmer for 25 years. A realization that has crept up on me in the last 5 is that not everyone thinks that functions should be short: there are two cultures, with substantial numbers of excellent programmers belonging to both. My question is: how do we maintain harmonious, happy, and productive teams when people can disagree strongly about this issue?
The short-functions camp holds that functions should be short, tend toward the declarative, and use abstraction/implementation-hiding to increase readability (i.e. separable subsections of the function body should often be broken out into well-named helper functions). As an example, look at Peter Norvig's beautiful https://github.com/norvig/pytudes. For a long time I thought that this was how all "good programmers" thought code should be written. Personally, I spent over a decade writing in a dynamic and untyped language, and the only way that I and my colleagues could make that stuff reliable was to write code adhering to the tenets of the short-function camp.
The long-functions camp is, admittedly, alien to me, but I'll try to play devil's advocate and describe it as I think its advocates would. It holds that lots of helper functions are artificial, and actually make it _harder_ to read and understand the code. They say that they like "having lots of context", i.e. seeing all the implementation in one long procedural flow, even though the local variables fall into non-interacting subsets that don't need to be in the same scope. They hold that helper functions destroy the linear flow of the logic, and that they should typically not be created unless there are multiple call sites.
The short-function camp also claims an advantage regarding testability.
Obviously languages play a major role in this debate: e.g. as mentioned above, untyped dynamic languages encourage short functions, and languages where static compilation makes strong guarantees regarding semantics at least make the long-function position more defensible. Expression-oriented and FP-influenced languages encourage short functions. But it's not obvious, e.g. Rust could go both ways based on the criteria just mentioned.
Anyway, more qualified people could and have written at much greater length about the topic. The questions I propose for discussion include
- Is it "just a matter of taste", or is this actually a more serious matter where there is often an objective reason for discouraging the practices of one or other camp?
- How can members of the different camps get along harmoniously in the same team and the same codebase?
-
Pytudes
I have the same impression. Reading the code, he uses global variables [1], obscure variable (k, bw, fw, x) and module names ("pal.py" instead of "palindromes.py"), doesn’t respect conventions about naming in general (uppercase arguments [2], which even the GitHub syntax highlighter is confused about). This feels like code you write for yourself to play with Python and don’t plan to read later.
Some parts of the code feel like what I would expect from a junior dev who started learning the language a couple weeks ago.
[1]: https://github.com/norvig/pytudes/blob/952675ffc70f3632e70a7...
[2]: https://github.com/norvig/pytudes/blob/952675ffc70f3632e70a7...
- Ask HN: Where do I find good code to read?
-
Using Prolog in Windows NT Network Configuration (1996)
Prolog is excellent for bikeshedding, in fact that might be its strongest axis. It starts with everything you get in a normal language such as naming things, indentation, functional purity vs side effects, where to break code into different files and builds on that with having your names try to make sense in declarative, relational, logical and imperative contexts, having your predicates (functions) usable in all modes - and then performant in all modes - having your code be deterministic, and then deterministic in all modes. Being 50 years old there are five decades of learning "idiomatic Prolog" ideas to choose from, and five decades of footguns pointing at your two feet; it has tabling, label(l)ing, SLD and SLG resolution to choose from. Built in constraint solvers are excellent at tempting you into thinking your problem will be well solved by the constraint solvers (it won't be, you idiot, why did you think that was a constraint problem?), two different kinds of arithmetic - one which works but is bad and one which mostly works on integers but clashes with the Prolog solver - and enough metaprogramming that you can build castles in the sky which are very hard to debug instead of real castles. But wait, there's more! Declarative context grammars let you add the fun of left-recursive parsing problems to all your tasks, while attributed variables allow the Prolog engine to break your code behind the scenes in new and interesting ways, plenty of special syntax not to be sneezed at (-->; [_|[]] {}\[]>>() \X^+() =.. #<==> atchoo (bless you)), a delightful deep-rooted schism between text as linked lists of character codes or text as linked lists of character atoms, and always the ISO-Standard-Sword of Damocles hanging over your head as you look at the vast array of slightly-incompatible implementations with no widely accepted CPython-like-dominant-default.
Somewhere hiding in there is a language with enough flexibility and metaprogramming to let your meat brain stretch as far as you want, enough cyborg attachments to augment you beyond plain human, enough spells and rituals to conjour tentacled seamonsters with excellent logic ability from the cold Atlantic deeps to intimidate your problem into submission.
Which you, dear programmer, can learn to wield up to the advanced level of a toddler in a machine shop in a mere couple of handfuls of long years! Expertise may take a few lifetimes longer - in the meantime have you noticed your code isn't pure, doesn't work in all modes, isn't performant in several modes, isn't using the preferred idiom style, is non-deterministic, can't be used to generate as well as test, falls into a left-recursive endless search after the first result, isn't compatible with other Prolog Systems, and your predicates are poorly named and you use the builtin database which is temptingly convenient but absolutely verboten? Plenty for you to be getting on with, back to the drawing boar...bikeshed with you.
And, cut! No, don't cut; OK, green cuts but not red cuts and I hope you aren't colourblind. Next up, coroutines, freeze, PEngines, and the second 90%.
Visit https://www.metalevel.at/prolog and marvel as a master deftly disecting problems, in the same way you marvel at Peter Norvig's Pytudes https://github.com/norvig/pytudes , and sob as the wonders turn to clay in your ordinary hands. Luckily it has a squeaky little brute force searcher, dutifully headbutting every wall as it explores all the corners of your problem on its eventual way to an answer, which you can always rely on. And with that it's almost like any other high level mostly-interpreted dynamic programming / scripting language.
What are some alternatives?
streamlit - Streamlit — A faster way to build and share data apps.
paip-lisp - Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"
voila - Voilà turns Jupyter notebooks into standalone web applications
asgi-correlation-id - Request ID propagation for ASGI apps
papermill - 📚 Parameterize, execute, and analyze notebooks
clerk - ⚡️ Moldable Live Programming for Clojure
voila-gridstack - Dashboard template for Voilà based on GridStackJS
nbmake - 📝 Pytest plugin for testing notebooks
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
PySimpleGUI - Python GUIs for Humans! PySimpleGUI is the top-rated Python application development environment. Launched in 2018 and actively developed, maintained, and supported in 2024. Transforms tkinter, Qt, WxPython, and Remi into a simple, intuitive, and fun experience for both hobbyists and expert users.
awesome-streamlit - The purpose of this project is to share knowledge on how awesome Streamlit is and can be
project-based-learning - Curated list of project-based tutorials