NumPy
crystal
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NumPy | crystal | |
---|---|---|
272 | 239 | |
26,360 | 19,110 | |
1.9% | 0.5% | |
10.0 | 9.8 | |
about 17 hours ago | 4 days ago | |
Python | Crystal | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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.
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]
crystal
- A Language for Humans and Computers
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Top Paying Programming Technologies 2024
27. Crystal - $77,104
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Crystal 1.11.0 Is Released
I like the first code example on https://crystal-lang.org
# A very basic HTTP server
- Is Fortran "A Dead Language"?
- Choosing Go at American Express
- Odin Programming Language
- I Love Ruby
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Ruby 3.3's YJIT: Faster While Using Less Memory
Obviously as an interpreted language, it's never going to be as fast as something like C, Rust, or Go. Traditionally the ruby maintainers have not designed or optimized for pure speed, but that is changing, and the language is definitely faster these days compared to a decade ago.
If you like the ruby syntax/language but want the speed of a compiled language, it's also worth checking out Crystal[^1]. It's mostly ruby-like in syntax, style, and developer ergonomics.[^2] Although it's an entirely different language. Also a tiny community.
[1]: https://crystal-lang.org/
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What languages are useful for contribution to the GNOME project.
Crystal is a nice language that's not only simple to read and write but performs very well too. And the documentation is amazing as well.
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Jets: The Ruby Serverless Framework
Ruby is a super fun scripting language. I much prefer it to python when I need something with a little more "ooomph" than bash. It's just...nice...to write in. Ruby performance has come a long way in the last decade as well. There's libraries for pretty much everything.
My modern programming toolkit is basically golang + ruby + bash and I am never left wanting.
I do find Crystal (https://crystal-lang.org/) really interesting and am hoping it has its own "ruby on rails" moment that helps the language reach a tipping point in popularity. All the beauty of ruby with all of the speed of Go (and then some, it often compares favorably to languages like rust in benchmarks).
What are some alternatives?
SymPy - A computer algebra system written in pure Python
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
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
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).
blaze - NumPy and Pandas interface to Big Data
go - The Go programming language
SciPy - SciPy library main repository
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
Numba - NumPy aware dynamic Python compiler using LLVM
mint-lang - :leaves: A refreshing programming language for the front-end web
Odin - Odin Programming Language