qiankun VS NumPy

qiankun - enables you and your teams to build next-generation and enterprise-ready web applications leveraging Micro Frontends. It is inspired by and based on single-spa. Ref- https://github.com/umijs/qiankun

https://bit.dev/ https://piral.io/ https://github.com/umijs/qiankun https://github.com/single-spa/single-spa

github qiankun

Others: FrintJS, qiankun, Berial, and Nuz

We will be using the package Qiankun (This is a micro frontend framework)

umijs/qiankun: 📦 🚀 Blazing fast, simple and completed solution for micro frontends.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

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:

numpy: A library for scientific computing in Python

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.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]