Bit VS Pandas

Our demo solution was built using Bit, which allows us to create shareable components, render component “previews,” generate component docs, and so on.

UI libraries come with their own set of challenges, which greatly limit their effectiveness. These challenges stem from more fundamental problems related to code sharing and reuse. Let’s explore some of these challenges and examine how a new entity, the Bit component, addresses them.

The following diagram showcases how bit shows the dependency graph of modified components and their dependents.

This tutorial demonstrates how to build a micro frontend application using Vite and Bit.

Bit: A next-generation build system for composable software.

Bit drives a paradigm shift in the way we structure our software and collaborate on code. Its component-based approach produces more maintainable projects and more effective collaboration. However, the power of Bit is best harnessed when you adopt a certain mindset. This blog aims to guide you through the core principles and methodologies of building software with independent Bit components.

And one such tool that I've found that supports this component-driven approach is Bit.

This blog will explain how Bit can be used to implement any architecture and transform “fatal” decisions that seem too hard to change into decisions that are easy to make and change.

Oh can I address theses issues. I already looked at tools like Nx or Bit, but they aren't matching our needs with closed source libs.

To begin, you need to prepare your environment. A few ways to build a React package include tools like Bit, Storybook, Lerna, and TSDX. However, for this tutorial, you will use a zero-configuration bundler for tiny modules called Microbundle.

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.