atrium
NumPy
| atrium | NumPy | |
|---|---|---|
| 1 | 272 | |
| 537 | 26,360 | |
| - | 0.9% | |
| 9.4 | 10.0 | |
| about 18 hours ago | 7 days ago | |
| Kotlin | Python | |
| GNU General Public License v3.0 or later | 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.
atrium
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Hacktoberfest: 69 Beginner-Friendly Projects You Can Contribute To
https://github.com/robstoll/atrium A multiplatform assertion library for Kotlin
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?
strikt - An assertion library for Kotlin
SymPy - A computer algebra system written in pure Python
Kotest - Powerful, elegant and flexible test framework for Kotlin with additional assertions, property testing and data driven testing
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
Kluent - Fluent Assertion-Library for Kotlin
blaze - NumPy and Pandas interface to Big Data
mockk - mocking library for Kotlin
SciPy - SciPy library main repository
hikaku - A library that tests if the implementation of a REST-API meets its specification.
Numba - NumPy aware dynamic Python compiler using LLVM
mockito-kotlin - Using Mockito with Kotlin
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).