bottleneck
NumPy
bottleneck | NumPy | |
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1 | 272 | |
1,006 | 26,413 | |
1.4% | 1.1% | |
3.5 | 10.0 | |
4 days ago | 6 days ago | |
Python | Python | |
BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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bottleneck
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Update on my Python, C++ and Rust Library
Fast Array Manipulation in Python: Since Numpy is the de facto standard for storing multi-dimensional data, any performance gain you see using librapid math kernels will need to be realized on data which probably started its life as a numpy array, and needs to be passed to another tool as a numpy array. Hopefully there will be (or already is?) a way to build a librapid array out of a numpy array without copying the data and vice versa. In fact I might suggest that librapid focus on the fast math operations and simply become an accelerator for numpy arrays. For instance, look at CuPy which provides GPU-implemented operations within a numpy-compatible API, and Bottleneck which simply provides fast C-based implementations of some otherwise slow parts of Numpy. Also note that numpy *can* be multi-threaded depending on the operation and some environment variables. Single-threaded to Single-threaded I think you will be hard-pressed to beat Numpy on general math operations, but that doesn't mean there aren't specific "kernels" that are more specialized that can be greatly improved with a C++ back-end.
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?
cupy - NumPy & SciPy for GPU
SymPy - A computer algebra system written in pure Python
pyxirr - Rust-powered collection of financial functions.
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
segyio - Fast Python library for SEGY files.
blaze - NumPy and Pandas interface to Big Data
jdupes - A powerful duplicate file finder and an enhanced fork of 'fdupes'.
SciPy - SciPy library main repository
trusted-traveler-scheduler - Python script for periodically fetching appointment dates from the Trusted Traveler Program API for Global Entry, Nexus, SENTRI, and FAST, with notifications to the user when new appointments are discovered.
Numba - NumPy aware dynamic Python compiler using LLVM
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).
manim - Animation engine for explanatory math videos