VS julia

Compare vs julia and see what are their differences.

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67 284
8,586 41,503
- 0.7%
2.3 10.0
19 days ago 3 days ago
Python Julia
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

Posts with mentions or reviews of We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-25.


Posts with mentions or reviews of julia. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-30.
  • Is Julia suitable today as a scripting language?
    6 projects | | 30 Jan 2023
    Something like this:
  • Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges
    10 projects | | 19 Jan 2023
    In addition to Structured Query Language(SQL), we can also use a variety of different programming languages, such as Python, Java, JavaScript, R, Julia, Scala, or any other programming language as long as it supports a basic database connection and functions to perform all of those operations, to connect to databases and perform more advanced query operations on the data. This gives us greater flexibility and allows us to apply custom-created logic to the data.
  • Go devs that learned Rust, what are your thoughts on it?
    7 projects | | 8 Jan 2023
  • Extending Python with Rust
    12 projects | | 27 Dec 2022
    There are some nice tools for 3D PDEs which connect to DiffEq like GridAP ( and Ferrite ( PDE tooling is where focus has been moving to as things evolve.

    As for JIT, just today there was a PR that was merged that makes Julia cache and reuse binaries of packages ( It won't be out until the next release of Julia, but it's a pretty major improvement to not JIT fully inferred package calls.

  • Ruby 3.2.0 Released
    4 projects | | 25 Dec 2022
  • Why isn’t Go used in AI/ML?
    8 projects | | 23 Dec 2022
    The Julia folk are trying to build a competitor. They’ve made a promising start but the Python ecosystem and is hard to beat. Also it’s a great REPL environment many data science folk like.
  • Startups are building with the Julia Programming Language
    3 projects | | 13 Dec 2022
    2. "every couple of years I try julia again, and every time it's still slow"

    On the other hand, these comments are replying to a post about a pull request [1] addressing "time to first X" (TTFX) by providing infrastructure to cache native code on a per package basis. For further background, please refer to a prior pull request merged last month which tackles the prerequisite step of supporting external linkage in system images [2]. The recent pull request also contains an evaluation from a "non-core" developer who is reviewing the pull request, sharing his real world experience with graphs, measurements, and comments about the documentation. To me this pull request is exemplary of how Julia development should work and how users can contribute to the process. Referring to the original article, I also notice that many of the companies and startups mentioned are involved with the development of the Julia language itself rather than merely the application of the language. Perhaps this ability to participate in Julia development at this stage is seen as a feature to these organizations.

    I'm unclear what the critique is here with rehasing these comments in response to a pull request. Should that pull request take another approach? Is code review on that pull request progressing too slowly? Is it that the priority should shift away from the compiler to provide further infrastructure for correctness, traits, or some other feature? There's a lost opportunity here to actually expand the conversation rather than reiterating the same arguments.

    Nonetheless, I thank thetwentyone for posting news about a substantive pull request addressing compilation based latency. I hope to hear more about these ongoing efforts.


    3 projects | | 13 Dec 2022
    Jumping the gun slightly, but TTFX likely about to get a lot better (5x+ improvement in times) in the next version (1.9). Via cachine of the native compiled code.

  • Guido van Rossum on types, speed, and the future of Python
    2 projects | | 12 Dec 2022
    In many cases it would be sufficient to have a few type annotations combined with type stable code in order for a compiler or type checker to infer most types. Examples for this are and .
  • Elixir-style Pipelines in 9 Lines of Ruby
    2 projects | | 10 Dec 2022
    > Julia's threading macro is surprisingly brittle, only letting you chain single-argument functions

    This combined with the link could make things confusing to a Julia beginner, so to make things clear:

    * The linked page is talking about an external Julia package which provides a macro. And that macro lets you use the `_` syntax similar to what you describe Racket as having.

    * Julia's default inbuilt threading/piping syntax is the one with the single-argument limitation, and that's an operator, not a macro.

    There's been a lot of discussion about bringing the `_` syntax or something like it to the base language, but there seem to be implementation difficulties. [1]

    > +R ::

    This page is talking about magrittr piping (which is probably still the most popular), but base R also got inbuilt piping syntax with version 4.1. IIRC, it automatically passes the piped-in value as the first argument to the subsequent function.


What are some alternatives?

When comparing and julia you can also consider the following projects:

jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

NetworkX - Network Analysis in Python

Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.

rust-numpy - PyO3-based Rust bindings of the NumPy C-API

Numba - NumPy aware dynamic Python compiler using LLVM

StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)

F# - Please file issues or pull requests here:

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).

JLD2.jl - HDF5-compatible file format in pure Julia

LUA - A programming language based upon the lua programming language

femtolisp - a lightweight, robust, scheme-like lisp implementation

awesome-lisp-companies - Awesome Lisp Companies