For people who didnt understand the phrase like myself: https://en.wikipedia.org/wiki/Thin_blue_line?wprov=sfla1

Codeberg keeps calling the group the far right. Is there any political motivation or something else here? To me, it just looks like troll behavior. Is there more details about the attacks that I missed?

This looks exciting. I hope the transition goes well.

I would say to get automated backups running on the first few before you do them all. Thats a luxury we get “for free” with cloud services.

Note on firefly iii. I use it because I’ve been using it, but after using it for 4ish years, I dont really recommend it. The way I use it anyway, I think inserting data could be easier (I do it manually on purpose) and the graphs/visualizations I also wish were better. My experience with search functionality is also sub par. I would look at other alternatives as well, but I think its still better than not tracking finances at all. But I wonder if using a database client to insert data and python scripts or grafana to analyze the data would be better for me…YMMV

Good luck!

I replaced my tabbyml code assistant this week with ollama+continue.dev. But I’m having issues with speed. I think this is because I switched from code qwen 2.5B (ish) to Deepeek Coder 9B (ish) and I think I’m pushing the limits of my GPU. Maybe I’ll spend today sorting out which models I want to use and which computers I want to use them on so I dont run into this issue (I’ve got ollama on 2 computers with 3 GPUs shared between them, for a total of 24GB VRAM)

Floccus just syncs, so whatever hasnt been synced would just wait in your bookmarks until the server was available.

I use borgbackup to create backups. I point backups to another home computer and borgbase.com. Borg itself is an amazing tool. I think you should learn how it works even if it doesnt end up being the best fit for you.

I’ve got a subset of my files encrypted and backed up using borg. It gets backed up to another computer in my home and then cloud storage via borgbase.com.

And how do you like yew? Long ago I used seed.rs, which was more like ELM than react. But I think that project has since gone unmaintained. I also tried yew when I think they were in the middle of a huge API transition. Do you think its easier to write in yew than it would be in react/vue/svelte?

Cursed tech stack and image. Project looks cool. Can you elaborate more on why you used rust for front end and python for backend? I would assume rust would have been more applicable for back end work. Then again, Im not familiar with fastAPI.

No I was being accusatory unfairly. I’ve updated my post.

Hey OP, how do you follow all these updates? Is it RSS feeds on these projects? You’re on top of it this morning.

First off, understanding the different data structure from a high level is mandatory. I would understand the difference between a dataframe, series, and index are. Further, learn how numpy’s ndarrays play a role.

From there, unfortunately, I had to learn by doing…or rather struggling. It was one question at a time to stack overflow, like “how to filter on a column in pandas”. Maybe in the modern era of LLMs, this part might be easier. And eventually, I learned some patterns and internalized the data structures.

You are correct. For some data sources like parquet it includes some metadata that helps with this, but it’s not as robust at databases I dont think. And of course, cvs have no metadata (I guess a header row.)

The actually specification for how to efficiently store tabular data in memory that also permits quick execution of filtering, pivoting, i.e. all the transformations you need…is called apache arrow. It is the backend of polars and is also a non-default backend of pandas. The complexity of the format I’m unfamiliar with.

I learned SQL before pandas. It’s still tabular data, but the mechanisms to mutate/modify/filter the data are different methodologies. It took a long time to get comfy with pandas. It wasnt until I understood that the way you interact with a database table and a dataframe are very different, that I started to finally get a grasp on pandas.

If it works, don’t fix it!

A big feature of polars is only loading applicable data from disk. But during exporatory data analysis (EDA) you often have the whole dataset in memory. In this case, filters wont help much there. Polars has a good page in their docs about all the possible optimizations it is capable of. https://docs.pola.rs/user-guide/lazy/optimizations/

One I see off the top is projection pushdown, which only selects relevant columns for a final transformations. In pandas, if you perform a group by with aggregation, then only look at a few columns, you still perform aggregation across all the data. In polars lazy API, you would define the entire process upfront, and it would know not to aggregate certain columns, for instance.

Imo Rust already has the perfect book. I would make a resource for C developers. Especially since you know C already.

Its a paradigm shift from pandas. In polars, you define a pipeline, or a set of instructions, to perform on a dataframe, and only execute them all at once at the end of your transformation. In other words, its lazy. Pandas is eager, which every part of the transformation happens sequentially and in isolation. Polars also has an eager API, but you likely want to use the lazy API in a production script.

Because its lazy, Polars performs query optimization, like a database does with a SQL query. At the end of the day, if you’re using polars for data engineering or in a pipeline, it’ll likely work much faster and more memory efficient. Polars also executes operations in parallel, as well.

How do you use Godot for data science?

~/repo for code I write and ~/src for code I didnt.