tidypolars
Apache Arrow
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tidypolars | Apache Arrow | |
---|---|---|
7 | 75 | |
309 | 13,523 | |
- | 2.2% | |
8.0 | 10.0 | |
3 months ago | about 22 hours ago | |
Python | C++ | |
MIT License | Apache License 2.0 |
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.
tidypolars
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Modern Polars: a side-by-side comparison with Pandas
I recommend trying tidypolars
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Modern Polars: an extensive side-by-side comparison of Polars and Pandas
There’s a tidypolars package that appears to be well-maintained https://github.com/markfairbanks/tidypolars
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R Tidyverse / dplyr is life changing!
tidypolars is one I’ve seen. Still very new, but it’s built on top of polars (which is a little more like dplyr to begin with), so it’s much faster than pandas.
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Introducing tidypolars - a Python data frame package with syntax familiar to R tidyverse users
If I misunderstood your question - feel free to open a discussion with a small code example and we can talk through how you can do it in tidypolars.
- Introducing tidypolars - a Python data frame package for R tidyverse users
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Tidyverse appreciation thread
Try out tidypolars. It's really close to tidyverse syntax and it's a lot faster than pandas as well
Apache Arrow
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How moving from Pandas to Polars made me write better code without writing better code
In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++.
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From slow to SIMD: A Go optimization story
I learned yesterday about GoLang's assembler https://go.dev/doc/asm - after browsing how arrow is implemented for different languages (my experience is mainly C/C++) - https://github.com/apache/arrow/tree/main/go/arrow/math - there are bunch of .S ("asm" files) and I'm still not able to comprehend how these work exactly (I guess it'll take more reading) - it seems very peculiar.
The last time I've used inlined assembly was back in Turbo/Borland Pascal, then bit in Visual Studio (32-bit), until they got disabled. Then did very little gcc with their more strict specification (while the former you had to know how the ABI worked, the latter too - but it was specced out).
Anyway - I wasn't expecting to find this in "Go" :) But I guess you can always start with .go code then produce assembly (-S) then optimize it, or find/hire someone to do it.
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Time Series Analysis with Polars
One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on par with its NumPy-based features. Polars was built around Arrow from the get go. This makes it very powerful when it comes to exchanging data with other languages and reducing the number of in-memory copying operations, thus leading to better performance.
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TXR Lisp
IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/
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3D desktop Game Engine scriptable in Python
https://www.reddit.com/r/O3DE/comments/rdvxhx/why_python/ :
> Python is used for scripting the editor only, not in-game behaviors.
> For implementing entity behaviors the only out of box ways are C++, ScriptCanvas (visual scripting) or Lua. Python is currently not available for implementing game logic.
C++, Lua, and Python all implement CFFI (C Foreign Function Interface) for remote function and method calls.
"Using CFFI for embedding" https://cffi.readthedocs.io/en/latest/embedding.html :
> You can use CFFI to generate C code which exports the API of your choice to any C application that wants to link with this C code. This API, which you define yourself, ends up as the API of a .so/.dll/.dylib library—or you can statically link it within a larger application.
Apache Arrow already supports C, C++, Python, Rust, Go and has C GLib support Lua:
https://github.com/apache/arrow/tree/main/c_glib/example/lua :
> Arrow Lua example: All example codes use LGI to use Arrow GLib based bindings
pyarrow.from_numpy_dtype:
- Show HN: Udsv.js – A faster CSV parser in 5KB (min)
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.
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Cap'n Proto 1.0
Worker should really adopt Apache Arrow, which has a much bigger ecosystem.
https://github.com/apache/arrow
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C++ Jobs - Q3 2023
Apache Arrow
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Wheel fails for pyarrow installation
I am aware of the fact that there are other posts about this issue but none of the ideas to solve it worked for me or sometimes none were found. The issue was discussed in the wheel git hub last December and seems to be solved but then it seems like I'm installing the wrong version? I simply used pip3 install pyarrow, is that wrong?
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
tidytable - Tidy interface to 'data.table'
h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
dtplyr - Data table backend for dplyr
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
db-benchmark - reproducible benchmark of database-like ops
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
extendr - R extension library for rust designed to be familiar to R users.
pandoc - Universal markup converter
ClickHouse - ClickHouse® is a free analytics DBMS for big data