pg8000
polars
pg8000 | polars | |
---|---|---|
5 | 144 | |
467 | 26,378 | |
- | 3.4% | |
7.4 | 10.0 | |
7 days ago | about 19 hours ago | |
Python | Rust | |
BSD 3-clause "New" or "Revised" 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.
pg8000
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How to run psycogp2 in Aws lambda?
As others have said you can use a custom compiled version of the lib, lambda layer or use lambda images, however, if you're not committed to psycogp2 I've found pg8000 a much easier library to work with in Lambda. You can just install it as any other library without any problems.
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Pure Python Distributed SQL Engine
When people say "pure X", to me, it normally means they didn't involve an FFI or external compiler. This is an often beneficial thing since it simplifies your build process.
For example, here [0] is a "pure Python postgres driver" and the implication is that it doesn't use libpg.
Or see also this discussion [1].
[0] https://github.com/tlocke/pg8000
[1] https://www.reddit.com/r/learnpython/comments/nktut1/eli5_th...
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FAQs: Why we donβt have them (2013)
I agree that information shouldn't be duplicated, but in one of my projects I've taken the opposite approach and made the FAQ the only place that certain information is presented. It's for the library https://github.com/tlocke/pg8000 and I've called them 'Examples' rather than a FAQ, but each time I get a question that isn't covered by the examples I add in a new example. I'd be interested to hear what people think of this approach.
- Pg8000 β Pure-Python PostgreSQL driver
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?
Django - The Web framework for perfectionists with deadlines.
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 π
sqlglot - Python SQL Parser and Transpiler
modin - Modin: Scale your Pandas workflows by changing a single line of code
opteryx - π¦ A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.
datafusion - Apache DataFusion SQL Query Engine
TheAlgorithms - All Algorithms implemented in Python
DataFrames.jl - In-memory tabular data in Julia
quokka - Making data lake work for time series
datatable - A Python package for manipulating 2-dimensional tabular data structures
sqlparser-rs - Extensible SQL Lexer and Parser for Rust
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing