awesome-fastapi
NumPy
| awesome-fastapi | NumPy | |
|---|---|---|
| 5 | 272 | |
| 7,494 | 26,360 | |
| - | 0.9% | |
| 4.8 | 10.0 | |
| about 2 months ago | 7 days ago | |
| 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-fastapi
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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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Flask Allowed Me to Implement My Startup for only $12.
This list has a lot of goodies
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⚡ FastAPI Websocket RPC and Pub/Sub packages
Was just about to suggest you add it to the Awesome FastAPI but another contributor already did. This looks very cool and I will definitely try it out on a project I am working on currently. Thank you!
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Project structure for scalable fastapi project.
You can find a number of large open source projects here: https://github.com/mjhea0/awesome-fastapi#open-source-projects.
- Good Resources for Learning FastAPI ?
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?
fastapi-boilerplate - A template repository to start your FastAPI backend projects.
SymPy - A computer algebra system written in pure Python
awesome-nodejs - :zap: Delightful Node.js packages and resources
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
fastapi_websocket_pubsub - Fork of https://github.com/permitio/fastapi_websocket_pubsub
blaze - NumPy and Pandas interface to Big Data
the-book-of-secret-knowledge - A collection of inspiring lists, manuals, cheatsheets, blogs, hacks, one-liners, cli/web tools and more.
SciPy - SciPy library main repository
fastango - Simplifies class-based views for more organized and maintainable code in FastAPI ✨
Numba - NumPy aware dynamic Python compiler using LLVM
databases - Async database support for Python. 🗄
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).