Top 8 Julia Python Projects

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• DifferentialEquations.jl

  6 2,850 7.1 Julia

  Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

  

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• PyCall.jl

  28 1,466 6.1 Julia

  Package to call Python functions from the Julia language

  

• pythonidae

  1 956 0.0 Julia

  Curated decibans of scientific programming resources in Python.

• PythonCall.jl

  3 782 8.4 Julia

  Python and Julia in harmony.

  

• PyPlot.jl

  4 476 6.6 Julia

  Plotting for Julia based on matplotlib.pyplot

  

• Conda.jl

  3 174 6.8 Julia

  Conda managing Julia binary dependencies

  

  Project mention: Can't use SymPy and PyCall | /r/Julia | 2023-12-06

  The Julia session need to be restarted. More information is available at https://github.com/JuliaPy/Conda.jl.

• CondaPkg.jl

  1 121 6.3 Julia

  Add Conda dependencies to your Julia project

  

• SciPyDiffEq.jl

  5 21 3.9 Julia

  Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization

  

  Project mention: Building a compile-time SIMD optimized smoothing filter | news.ycombinator.com | 2024-09-28

  Did you link the wrong script? The script you show runs everything to statistical significance using Chairmarks.@b.

  Also, I don't understand what the issue would be with mixing Python and Julia code in the benchmark script. The Julia side JIT compiles the invocations which we've seen removes pretty much all non-Python overhead and actually makes the resulting SciPy calls faster than doing so from Python itself in many cases, see for example https://github.com/SciML/SciPyDiffEq.jl?tab=readme-ov-file#m... where invocations from Julia very handily outperform SciPy+Numba from the Python REPL. Of course, that is a higher order function so it's benefiting from the Julia JIT in other ways as well, but the point is in previous benchmarks we've seen the overhead floor as so low (~100ns IIRC) that it didn't effect benchmarks negatively for Python and actually improved many Python timings in practice. Though it would be good to show in this case what exactly the difference is in order to isolate any potential issue, I would be surprised if it's more than a 100ns overhead for an invocation like this and with 58ms being the benchmark size, that cost is well below the noise floor).

  Though trying different datasets is of course valid. There's no reason to reject a benchmark just because it doesn't fit into L3 cache, there's many use cases for that. But it does not mean that all use cases are likely to see such a result.

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• A note from our sponsor - SaaSHub
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