pygwalker VS devpod

The most popular data visualization python library in 2023. It turns your dataframe into an interactive data exploration app like tableau/powerBI with one line of code. It provides simple drag-and-drop/chat interface for you to build charts. It can run in juypter notebook, which means you do not need to switch between your code and the visualization app. Besides, you can also build interactive spitial visualization on maps with it. And it also has Javascript and R version.

GWalkR is the R binding of Graphic-Walker, if you want to use it in python, check the python version: PyGWalker: https://github.com/Kanaries/pygwalker

+ Graphic Walker (https://github.com/Kanaries/graphic-walker): A lite embeddable component for visual analysis.

+ PyGWalker (https://github.com/Kanaries/pygwalker): turning your pandas dataframe into a Tableau-style User Interface for visual exploration.

RATH is a collection of interesting ideas that we think the next generation of data analysis software should be, so there might be many features that not well organized to be a united app. Tell me which feature you prefer and which is not. Looking forward for your ideas and advice.

When you push your code to Github, you can develop the app using codespace and it will automatically set up an online development environment for you. Other tools will make your life easier when developing using a dev container e.g. DevPod.

DevPod is that new kid in town that works on the same standard of devcontainers.json that Codespaces uses but is on the infrastructure of your choice and is open source. The project was just launched this May and has gathered more than 5.3K stars in this period. The advantage is the lower costs (around 5-10 times cheaper than cloud VMs) with auto-shutdown.

For students, unless there are allocated server resources with network access, it SHOULD/MUST scale down to one local offline ARM64 node (because school districts haven't afforded containers on a managed k8s cloud for students at scale fwiu, though universities do with e.g. JupyterHub and BinderHub [4] and Colab).

For Chromebook sysadmins, Instructors, and Students learning about how {Linux*, ChromiumOS, Android, Git, Bash, ZSH, Python, and e.g. PyData Tools supported by NumFOCUS} are developed, for example;

When you git commit to a git branch, and then `git push` that branch to GitHub, and create a Pull Request, GitHub Actions runs the (container,command) tasks defined in the YAML files in the .github/workflows/ directory of the repo; so `git push` to a PR branch runs the CI job and the results are written back as cards in the Pull Request thread on the GitHub Project; saving to the server runs the (container,command) Actions with that revision of the git repo.

Somewhat-equivalent GitOps CI Continuous Integration workflows (without Bazel or Blaze or gtest or gn, or GitHub Enterprise or GitHub Free due to the kids' intererests) that might be supported at least in analogue by Education and Chromebooks: k8s with podman-desktop in a VM, Gitea Actions (nektos/act; like Github Actions), devpod

devpod: https://github.com/loft-sh/devpod :

> Codespaces but open-source, client-only and unopinionated: Works with any IDE and lets you use any cloud, kubernetes or just localhost docker. (with devcontainer.json, like Github Codespaces)

devcontainer.json is supported by a number of tools; e.g. VScode, IntelliJ,: https://containers.dev/supporting

repo2docker has buildpacks (like Heroku and Google AppEngine).

repo2docker buildpacks should probably work with devcontainer.json too?

repo2docker docs > Usage > "REES: Reproducible Execution Environment" describes what all repo2docker will build a container from: https://repo2docker.readthedocs.io/en/latest/usage.html

jupyterhub/repo2docker builds a Dockerfile (Containerfile) from git repo (or a Figshare/Zenodo DOI) that minimally has at least an /environment.yml and /example.py (and probably also at least a /README.md to start with), and installs a current, updated version of jupyter notebook along with whatever's in e.g. /environment.yml per the REES spec. [1][2][3]

[1] repo2docker/buildpacks/base.py: https://github.com/jupyterhub/repo2docker/blob/main/repo2doc...

[2] "Make base_image configurable" https://github.com/jupyterhub/repo2docker/commit/20b08152578...

[3] repo2docker/buildpacks/conda/environment.py-3.11.yml:

Also see https://devpod.sh/ which has had quite a lot of exposure on HN recently.

For these reasons, I believe most developer environments should prioritize developer experience over fidelity. Tools like Containerized development environments and cloud emulators can strike the right balance and there’s no surprise that we see increased activity around devcontainers, and similar solutions.

Have you considered devcontainers?

Its use results in carrying entire development environments with you, while not cluttering your host OS.

Using DevPod (https://devpod.sh/) ypu are not locked into Visual Studio or Visual Studio Code, but you can use whatever tool you want.

IMO this kind of setup will provide a much better DX than running a bunch of VMs eating away the resources of your laptop.