discord.js VS Pandas

Now that you've installed Node.js and know how to open your console and run commands, you can finally install discord.js!

We're beyond excited to unveil RoboPlay! It's not just another hosting service; it's a next-gen platform tailored for Discord.js bots, especially those powered by the Robo.js framework.

Context: Robo.js is a framework designed for creating Discord.js bots with ease and best practices by default. Plugins extend your bot's features right away!

Note the video is 2 years old. Discord.JS is constantly changing alongside the Discord API. This is why you should follow official and up to date guides and not just copy paste off of youtube: https://discord.js.org/

The feature is not yet supported, but you can check the pull request for the feature here

Well, I recommend you JavaScript, it is good for beginners and there's a lot of content on internet. Once you understand and practice the basics, you can use discord.js to write your bots. They have a guide website if you are interested.

You can (with some effort) create your own bot using a library like Discord.js to use webhooks easier.

I want to dive into discord bot things but I was confused when I saw 2 sites of discordjs. There is https://discord.js.org/ and https://old.discordjs.dev/#/. Is the old one is no more supported? Which one to use? I couldn't understand the difference between them.

The GitHub issue that someone raised in the library can be found here: https://github.com/discordjs/discord.js/issues/9185

Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.

In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.