awesome-django
NumPy
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awesome-django | NumPy | |
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22 | 272 | |
8,440 | 26,290 | |
- | 1.4% | |
7.8 | 10.0 | |
11 days ago | about 14 hours ago | |
HTML | Python | |
Creative Commons Zero v1.0 Universal | GNU General Public License v3.0 or later |
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.
awesome-django
- I want to create IMDB for Open source projects
- Django/Rest Open Source project to simulate real job software/project after building some basic Django apps
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Marketing for Developers
awesome-django: a curated list of awesome Django projects; submit your own by making a PR.
- Django Roadmap
- Cool Github repositories for Everyone
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a list of useful extensions or something?
I'd be curious to know where others look. I usually start at awesome-django and often check django packages.
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What are some amazing, great python external modules, libraries to explore?
Aweome asyncio Awesome Django Awesome FastAPI
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Continuing with Django 4.0
One of my favorite things to do is to check out the latest Awesome Django projects
- Como avançar no conhecimento?
- Curated list of django related blogs.
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?
Misago - Misago is fully featured modern forum application that is fast, scalable and responsive.
SymPy - A computer algebra system written in pure Python
awesome-jmeter - A collection of resources covering different aspects of JMeter usage.
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
Strapi - 🚀 Strapi is the leading open-source headless CMS. It’s 100% JavaScript/TypeScript, fully customizable and developer-first.
SciPy - SciPy library main repository
awesome-creative-coding - Creative Coding: Generative Art, Data visualization, Interaction Design, Resources.
blaze - NumPy and Pandas interface to Big Data
redwood - The App Framework for Startups
Numba - NumPy aware dynamic Python compiler using LLVM
SoundBoard
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).