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.

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?

I used Zola for a while, but at the end of the day there wasnt enough themes available that fit what I was looking for. I ended up messing with the templating engine to get what I needed.

I suggest OP choose Hugo over Zola, in the hopes that they find a theme that suits them best and for the most part prevents them from having to touch templating to begin with.

Thats a wonderful article. Thank you for sharing

Paperlessngx will store pdfs and index their contents for searching. It’s not necessarily meant for books but I think it would work.

I’ve been using micromamba/mamba and not had solving issues like I did with conda. Im glad conda integrated libmamba.

Question: why were docker containers deemed security risks?

I recently built a site with hugo. Its very easy. You pick a theme, then write some markdown files. And when you need flexibility, you have it for later. I also think it’s the most popular right now, which lends to a lot of themes to pick from and a lot of cpmmunity support.

I’ve used minio briefly, and I’ve never used any other self hosted object storage. In the context of spinning it up with docker, it’s pretty easy. The difficult part in my project was that I wanted some buckets predefined. The docker image doesn’t provide this functionality directly, so I had to spin up an adjacent container with the minio cli that would create the buckets automatically every time I spun up minio.

But for your use case you would manage bucket creation manually, from the UI. It seems straight forward enough, and I don’t have complaints. I think it would work for your use case, but I can’t say its any worse or better than alternatives.

Big fan of the reader mode changes. I’ll probabky start using it more often, not just on sites with horrendous popups.