Calculadora
NumPy
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Calculadora | NumPy | |
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1 | 272 | |
0 | 26,083 | |
- | 2.1% | |
0.0 | 10.0 | |
over 1 year ago | 7 days ago | |
Python | Python | |
The Unlicense | GNU General Public License v3.0 or later |
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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.
Calculadora
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-A calculator
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
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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
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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]
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Beginning Python: Project Management With PDM
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example I've decided to expand our math functionality with NumPy. pdm add is what's used to add dependencies like this to our project:
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Building an efficient sparse keyword index in Python
Large computations in pure Python can also be painfully slow. Luckily, there is a robust landscape of options for numeric processing. The most popular framework is NumPy. There is also PyTorch and other GPU-based tensor processing frameworks.
What are some alternatives?
SymPy - A computer algebra system written in pure Python
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
SciPy - SciPy library main repository
blaze - NumPy and Pandas interface to Big Data
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
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
scikit-learn - scikit-learn: machine learning in Python
Scrapy - Scrapy, a fast high-level web crawling & scraping framework for Python.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration