pyo3-polars
polars
| pyo3-polars | polars | |
|---|---|---|
| 3 | 144 | |
| 201 | 26,514 | |
| 7.0% | 3.9% | |
| 7.3 | 10.0 | |
| about 1 month ago | 3 days ago | |
| Rust | Rust | |
| MIT License | 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.
pyo3-polars
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Introducing polars expression plugins for python
See the full examples here: https://github.com/pola-rs/pyo3-polars/tree/main/example/derive_expression
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Polars query engine 0.29.0 released
Something like this? https://github.com/pola-rs/pyo3-polars
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Easiest way to get a pandas dataframe from python to rust?
If you use polars, you can go between rust and python for free within the same process. I have made pyo3 extensions to make this easier: https://github.com/pola-rs/pyo3-polars
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?
ZenithTA - A high performance python technical analysis library written in Rust and the Numpy C API.
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 🚀
delta-rs - A native Rust library for Delta Lake, with bindings into Python
modin - Modin: Scale your Pandas workflows by changing a single line of code
datafusion - Apache DataFusion SQL Query Engine
DataFrames.jl - In-memory tabular data in Julia
datatable - A Python package for manipulating 2-dimensional tabular data structures
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
db-benchmark - reproducible benchmark of database-like ops
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
hdf5-rust - HDF5 for Rust
tidypolars - Tidy interface to polars