deffcode VS static-frame

Vidgear and new deffcode library are my best. I bet you don't know none of them. But they're pretty awesome when it comes to video-processing and stuff.

DeFFcode is a cross-platform High-performance Video Frames Decoder that flexibly executes FFmpeg pipeline inside a subprocess pipe for generating real-time, low-overhead, lightning fast video frames with robust error-handling in just a few lines of python code.

GitHub Project link: https://github.com/abhiTronix/deffcode

Currently FFdecoder API's support for WriteGear API is still in beta and can cause very high CPU usage(even through the given example will work without any errors). Kindly use OpenCV's VideoWriter Class until this issue is resolved. However, this will change in upcoming commits as I'm already working on it. Kindly watch DeFFcode GitHub Repository to get updates instantly. Good luck!

📚 Documentation: https://abhitronix.github.io/deffcode

This is inadequate, as it ignores the types contained within the container. A DataFrame might have string column labels and three columns of integer, string, and floating-point values; these characteristics define the type. A function argument with such type hints provides developers, static analyzers, and runtime checkers with all the information needed to understand the expectations of the interface. StaticFrame 2 now permits this:

StaticFrame is an alternative DataFrame library that offers efficient solutions to this problem, both for in-memory and disk-based memoization.

A NumPy array is a Python object that stores data in a contiguous C-array buffer. The excellent performance of these arrays comes not only from this compact representation, but also from the ability of arrays to share "views" of that buffer among many arrays. NumPy makes frequent use of "no-copy" array operations, producing derived arrays without copying underling data buffers. By taking full advantage of NumPy's efficiency, the StaticFrame DataFrame library offers orders-of-magnitude better performance than Pandas for many common operations.

static-frame. An immutable alternative to pandas.

StaticFrame is an immutable DataFrame library that offers solutions to such problems. In StaticFrame, alternative fill value representations can be used to preserve columnar types in reindexing, shifting, and many other operations that require fill_value arguments. For operations on heterogeneously typed columnar data, one fill value is simply not enough.

For me, one example of high cpu usage is when i visit links like this one (https://github.com/InvestmentSystems/static-frame/blob/master/static_frame/performance/core.py) on GitHub. Safari is extremely laggy when i do nothing more than just scrolling around. Do you have sth like this?