Python Packages Project Generator
NumPy
Python Packages Project Generator | NumPy | |
---|---|---|
5 | 272 | |
1,064 | 26,459 | |
- | 1.2% | |
0.0 | 10.0 | |
8 months ago | 1 day ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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.
Python Packages Project Generator
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Which scaffolding package should I use?
- python-package-template
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Show HN: Go-template – A Cookiecutter template for Go
Hey HN, this would be more of an early release (still planning on some tweaks before a release) -- would love to hear your thoughts on this!
For some back-story, this is more of a side-side-project (made this while working on another side-project).
When I switched to using Go for my projects (from Python), the lack of a template generator similar to python-package-template[1] was very annoying. I would copy the basic files (Makefile, Github actions, PR templates, etc) from the previous project only to realize I forgot to change some stuff, and now would need to rewrite git history.
By the third project, I decided to create a template generator for Go! I've tried to keep the generated project as flexible as possible - you can decide to skip the of it and go for a simple project, or take the bloat (pre-commit would need Python for one).
While making go-template, one of my side goals has been to keep the project beginner-friendly. I remember stumbling upon python-package-template[1] as a novice, and learning more than I had in a semester - Makefiles, linters, code-formatters, semantic versioning, pipelines, and so much more! With go-template, I hope to give that same experience to some other newbie who might stumble upon my repo (or a project generated using go-template).
As a fun fact, go-template has an option to remove Github-specific-features (pull request templates, workflows, etc). This was inspired by a comment on HN[2] pointing out that many open-source projects were on Github simply because of FOMO, which in-turn promoted Github's dominance!
[1]: https://github.com/TezRomacH/python-package-template
- Python Best Practices for a New Project in 2021
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My humble try to make a language-independent tool for boilerplate generation
Oh, and if I am not mistaken, you have also used the python-package-template itself to generate goli structure 🔥
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit?
CookieCutter or Kedro are the winners. I still think we will stick to Kedro template, because it offers extra functionality, and I like to think of each project as a set of pipelines to be run. Anyway, some cookiecutter templates are very good, like this one. In case we use both Kedro and ClearML, we'll have to figure out how to integrate its pipelines with ClearML tasks. But in the slack channel of ClearML there are other teams doing the same, so at least it's possible.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
Poe the Poet - A task runner that works well with poetry.
SymPy - A computer algebra system written in pure Python
warehouse - The Python Package Index
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
bandersnatch
blaze - NumPy and Pandas interface to Big Data
devpi
SciPy - SciPy library main repository
localshop - local pypi server (custom packages and auto-mirroring of pypi)
Numba - NumPy aware dynamic Python compiler using LLVM
python-decouple - Strict separation of config from code.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).