opteryx
polars
| opteryx | polars | |
|---|---|---|
| 1 | 144 | |
| 43 | 26,378 | |
| - | 3.4% | |
| 9.8 | 10.0 | |
| 6 days ago | 1 day ago | |
| Python | Rust | |
| Apache License 2.0 | 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.
opteryx
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Pure Python Distributed SQL Engine
Thanks for sharing.
I have a SQL Engine in Python too (https://github.com/mabel-dev/opteryx). I focused my initial effort on supporting SQL statements and making the usage feel like a database - that probably reflects the problem I had in front of me when I set out - only handling handfuls of gigabytes in a batch environment for ETLs with a group of new-to-data-engineering engineers. Have recently started looking more at real-time performance, such as distributing work. Am interesting in how you've approached.
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?
quokka - Making data lake work for time series
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 🚀
nomad - Deprecated and re-branded as Alto
modin - Modin: Scale your Pandas workflows by changing a single line of code
influxdb3-python - Python module that provides a simple and convenient way to interact with InfluxDB 3.0.
datafusion - Apache DataFusion SQL Query Engine
pg8000 - A Pure-Python PostgreSQL Driver
DataFrames.jl - In-memory tabular data in Julia
datafusion-ballista - Apache Arrow Ballista Distributed Query Engine
datatable - A Python package for manipulating 2-dimensional tabular data structures
emr-serverless-samples - Example code for running Spark and Hive jobs on EMR Serverless.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing