data_algebra
Codd method-chained SQL generator and Pandas data processing in Python. (by WinVector)
polars
Dataframes powered by a multithreaded, vectorized query engine, written in Rust (by ritchie46)
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| data_algebra | polars | |
|---|---|---|
| 5 | 144 | |
| 113 | 26,218 | |
| 0.9% | 6.7% | |
| 8.5 | 10.0 | |
| 6 months ago | 1 day ago | |
| Python | Rust | |
| BSD 3-clause "New" or "Revised" License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
data_algebra
Posts with mentions or reviews of data_algebra.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-01.
- Control Pandas, Polars, or SQL from One DSL
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Modern Pandas (Part 2): Method Chaining
There are a number of packages in Python specializing in variations of piped processing in Pandas. My own is this one: https://github.com/WinVector/data_algebra .
- Plotting Multiple Curves in Python
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Siuba – A Dplyr Port to Python
Neat. I've been working on my own "piped-Codd" style system I call the "data algebra" https://github.com/WinVector/data_algebra
I use method chaining as the composing notation.
polars
Posts with mentions or reviews of polars.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-08.
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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)