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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?
Big fan of the reader mode changes. I'll probabky start using it more often, not just on sites with horrendous popups.
Sometimes the app just shows a barcode that they scan. I always screenshotted the barcode and deleted the app. Better yet, save the barcode in catima https://catima.app/
To be fair, you're taking on a lot of new things at once. You can spin up docker containers on windows too, all while using a UI. I think it's great your exposing yourself to self hosting, linux, command line interface, and containerization all at once, but don't beat yourself up for it taking longer than expected. A lot of it takes time. I encourage you to keep trying and playing. Good luck!
Theres so many. Check out the awesome list: https://github.com/awesome-selfhosted/awesome-selfhosted
I think your stategy should be one service at a time. Do everything in docker, and start by tackling a simpler service. For example, you should try paperless-ngx. Absolute game changer. I didnt realize how much managing ny own directory structure sucked until I used this. Then, grow your service list more and more!
You'd have to explain how gimp doesnt suit your needs, because in the open source world its best in class for photo editing.
Examining my disk partitions with df is ruined now. Every snap gets its own virtual disk.
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rutrum
joined 2 years ago
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.