graphblas-algorithms
NumPy
graphblas-algorithms | NumPy | |
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3 | 272 | |
62 | 26,360 | |
- | 0.9% | |
5.7 | 10.0 | |
about 1 month ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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graphblas-algorithms
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What can I contribute to SciPy (or other) with my pure math skill? I’m pen and paper mathematician
And algorithms for the NetworkX backend graphblas-algorithms is here: https://github.com/python-graphblas/graphblas-algorithms
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NetworkX 3.0
GraphBLAS is wrapped by https://github.com/python-graphblas/python-graphblas/ and the algorithms are available at https://github.com/python-graphblas/graphblas-algorithms. NetworkX only dispatches the computation for a subset of algorithms to graphblas-algorithms right now.
If you want the graphblas API in python, https://github.com/python-graphblas/python-graphblas/ is the right place :)
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GraphBLAS
GraphBLAS is underrated and underused IMHO. If you use e.g. scipy.sparse, NetworkX, or similar, you should check out GraphBLAS. It is really fast even compared to scipy.sparse, and more capable in many ways.
They've actually started implementing the NetworkX API
https://github.com/python-graphblas/graphblas-algorithms
with python-graphblas
https://github.com/python-graphblas/python-graphblas
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?
cugraph - cuGraph - RAPIDS Graph Analytics Library
SymPy - A computer algebra system written in pure Python
netSALT - Simulation of lasing networks with quantum graphs and SALT theory.
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
blaze - NumPy and Pandas interface to Big Data
parallel-workers - run a work graph in parallel
SciPy - SciPy library main repository
Numba - NumPy aware dynamic Python compiler using LLVM
devops-schedule - how do you order a tree of work correctly where there are dependencies between works
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).