sxiv
polars
| sxiv | polars | |
|---|---|---|
| 3 | 144 | |
| 1,734 | 26,378 | |
| - | 3.4% | |
| 10.0 | 10.0 | |
| over 1 year ago | about 19 hours ago | |
| C | Rust | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sxiv
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What are you rewriting in rust?
i'm just start exploring Rust GUI world and currently i try to write an image viewer like sxiv with iced
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[herbstluftwm] g̶̼̟̓̒ĺ̶̦̠̿i̷̮̫͌t̵̻̋̄c̸͍̙̊h̵̦͛͜ all the things
image viewer: sxiv
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Switching from Linux. Need help.
I tried that and didn't like it, I highly recommend checking out sxiv.
polars
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
What are some alternatives?
feh - a fast and light image viewer
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
vimiv-qt - An image viewer with vim-like keybindings
modin - Modin: Scale your Pandas workflows by changing a single line of code
ueberzug - ueberzug is a command line util which allows to display images in combination with X11. The user is expected to have knowledge of theoretical computer science. https://github.com/seebye/ueberzug/wiki/Troubleshooting/119e30f331799b30fb9594db29740685cb09425b
datafusion - Apache DataFusion SQL Query Engine
image-roll - Image Roll - simple and fast GTK image viewer with basic image manipulation tools. Written in Rust.
DataFrames.jl - In-memory tabular data in Julia
zathura - Document viewer
datatable - A Python package for manipulating 2-dimensional tabular data structures
buku - :bookmark: Personal mini-web in text
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing