polars
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| normconf2022 | polars | |
|---|---|---|
| 2 | 144 | |
| 0 | 26,043 | |
| - | 6.1% | |
| 10.0 | 10.0 | |
| over 1 year ago | 4 days ago | |
| HTML | Rust | |
| MIT License | MIT License |
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.
normconf2022
- Replacing Pandas with Polars. A Practical Guide
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PRQL a simple, powerful, pipelined SQL replacement
The last example in this notebook [0] shows how similar PRQL is to dplyr. The rest of the notebook shows how you can use PRQL from R, Python and the command line.
[0]: https://github.com/snth/normconf2022/blob/main/notebooks/nor...
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?
FunSQL.jl - Julia library for compositional construction of SQL queries
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 🚀
ddl-diff - Generates SQL migrations by parsing and diffing DDL
modin - Modin: Scale your Pandas workflows by changing a single line of code
trustfall - A query engine for any combination of data sources. Query your files and APIs as if they were databases!
arrow-datafusion - Apache DataFusion SQL Query Engine
cargo-semver-checks - Scan your Rust crate for semver violations.
DataFrames.jl - In-memory tabular data in Julia
prql-query - Query and transform data with PRQL
datatable - A Python package for manipulating 2-dimensional tabular data structures
ArangoDB - 🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing