r-place-blender VS Pandas

Here's a link to the repo: https://github.com/ChrisCrossCrash/r-place-blender

You can see exactly how it works by looking at a Jypyter Notebook I made for this project.

Here is his GitHub repo. It's absolutely amazing. I didn't understand much of what it did but just followed his instructions and here is the end product.

I don't have that exact data on hand, but I could find out by re-doing this simulation and looking at the height map images. The tallest pixel would be the brightest pixel on any of the frames. Here's the details on how I made those heat maps.

The GitHub repository has all the needed files and guides needed to make animations like this. I've made it so that the repository contains a single frame, so if you wanted, you could download the repository as a zip file and open r-place.blend and it will work right out of the box! Blender is also a free download, so its easy and free to get started, without even having to code. The README in the repository has links to beginner video tutorials for Python and Blender. Let me know if you need any help!

Credit to /u/chris_cross_crash for developing the base code to generate the necessary color/heat files. Please go check out his GitHub to learn more! He did a great job documenting it, and it's very easy to replicate yourself!

I linked to a Kaggle dataset in the GitHub repo. It's a nice starting point, since that data is less than 1/10th the size and you can skip the trimming and formatting steps.

If you want, you could load up the data and find out. Here's my Jupyter Notebook explaining how to do that.

Here ya go! It would also be very helpful if you upvoted my new post about it :)

I've prepared a GitHub repository with everything you need to create beautiful 3D renders of the r/Place 2022 canvas for free!

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.