chat-replay-downloader VS NumPy

you can use this one chat downloader instead of pytchat for getting messages in the chat, they also have found a way to enable to get membership chats which we use in our server

YouTube Chat Downloader: https://github.com/xenova/chat-downloader

Acquisition of raw data : chat-downloader

It is fairly easy to write a script in Python to generate SRT files however, so you could probably integrate it into yt-dlp somehow. See also https://github.com/xenova/chat-downloader/issues/117 for an example of how this may be done, if you're into that sort of thing.

Credit : python with numpy, matplotlib, scipy, jupyter, chat-downloader

There's a solution for downloading chat replays but I can't get it to work and don't know enough about Python to figure it out like I did with youtube-dl. Comment archives are less simple than they seem aside from copy-pasting, since sites like wayback machine can't seem to get over the paywall like the other two solutions can.

by using chat_downloader i was able to run it with the message_types parameter like this:

chat-downloader was used to fetch chat data from the council stream.

Other resources: Youtube chat-downloader Python Wordcloud Library

https://github.com/xenova/chat-downloader Allows downloading of a live chat from YouTube or Twitch.

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]