numpy-groupies
gonum
| numpy-groupies | gonum | |
|---|---|---|
| 1 | 24 | |
| 191 | 7,272 | |
| - | 0.6% | |
| 5.9 | 8.3 | |
| 15 days ago | 3 days ago | |
| Python | Go | |
| BSD 2-clause "Simplified" License | BSD 3-clause "New" or "Revised" License |
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numpy-groupies
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From Python to NumPy
I have had the same experience and instead of Pandas have been using numpy-groupies to handle aggregate/groupby operations. It's quite performant and feels a bit cleaner to use than importing pandas for a couple operations.
https://github.com/ml31415/numpy-groupies
gonum
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How to set up interface to accept multi-dimension array?
But if you want to see what can be done for numeric stuff, check out gonum. Personally, I still wouldn't use Go, and I rather suspect it's still pretty easy to reach for something like what you're trying to do and not find it because Go just can't write that type sensibly, but you can at least see what is available, written by people who disagree with me about Go not being a great language for this.
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packages similar to Pandas
Numpy functionality is largely covered by https://www.gonum.org/ but for pandas I'm not sure if there is an equivalent as widely accepted. However, you might try https://github.com/rocketlaunchr/dataframe-go which I have not tried but it looks like it covers some of what you're looking for
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What libraries are missing?
Math libraries. It's just gonum right now. Missing things that often require people to link C or Python libs. E.g. https://github.com/gonum/gonum/issues/354
- Gonum Numerical Packages
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SIMD Accelerated vector math
Maybe this way you could avoid having Mul, Mul_Inplace, Mul_Into variants. Gonum mostly follows the same pattern.
- Modern hardware is fast, so let's choose the slowest language to balance it out
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graph: A generic Go library for creating graph data structures and performing operations on them. It supports different kinds of graphs such as directed graphs, acyclic graphs, or trees.
How does this compare to gonum graph? https://github.com/gonum/gonum/tree/master/graph
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From Python to NumPy
Go is quite a bit cleaner than Python and its concurrency/parallelism primitives can be well suited to scientific workloads.
You may want to have a look at Gonum (https://www.gonum.org), and the Go HEP package developed by CERN (https://go-hep.org).
I was also surprised to see DSP and pretty sophisticated packages, although I never used them: https://awesome-go.com/science-and-data-analysis
And of course Go has Jupyter integration, it's almost like running a script thanks to its fast compilation time.
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Go for science?
You should check out this https://github.com/gonum/gonum
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What makes concurrency in Go better than multiprocesing/multithreading in Python?
No, using CPU extensions and GPUs is a different thing than doing multitasking. There is Gonum but it is still slower than Numpy: https://github.com/gonum/gonum/issues/511
What are some alternatives?
ruck - 🧬 Modularised Evolutionary Algorithms For Python with Optional JIT and Multiprocessing (Ray) support. Inspired by PyTorch Lightning
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
hep - hep is the mono repository holding all of go-hep.org/x/hep packages and tools
gosl - Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, differential equations.
awkward - Manipulate JSON-like data with NumPy-like idioms.
Stats - A well tested and comprehensive Golang statistics library package with no dependencies.
python-performance-playground - Performance analysis of Python snippets for scientific computing
gonum/plot - A repository for plotting and visualizing data
PiHex - PiHex Library, written in Go, generates a hexadecimal number sequence in the number Pi in the range from 0 to 10,000,000.
goraph - Package goraph implements graph data structure and algorithms.
pandas - Flexible and powerful data analysis / manipulation library for Go+, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
gophernotes - The Go kernel for Jupyter notebooks and nteract.