peaks-framework
polars
| peaks-framework | polars | |
|---|---|---|
| 8 | 145 | |
| 46 | 26,779 | |
| - | 4.9% | |
| 10.0 | 10.0 | |
| about 1 year ago | 6 days ago | |
| Go | Rust | |
| MIT 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.
peaks-framework
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HTML5 Web Pivot/Drilldown Viewer for Polars.exe
If there are a strong demand on this toy, I will request Polars team to support my development on this matter after I completing my important task to publish Peaks.exe runtime to the github github.com/hkpeaks/peaks-framework
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Leverage A-SQL Statement to Accelerate ETL Processing
Github: github.com/hkpeaks/peaks-framework
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Databricks Clusters
CSV is splittable. This project is playing splittable csv github.com/hkpeaks/peaks-framework
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Test On 4 Concurrent Jobs Using Python-Polars 0.17.11 to GroupBy Billion Rows
This project has only 3-month history, first trial vesion to be released in Jun, provide most fundamental commands. For further info, you can visit github.com/hkpeaks/peaks-framework
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Converting my new code (Bytearray2Float64) into 19 Programming Language
In my previous dotnet pivot table web, I have implemented a in-memory data object serialized in disk. You can find the source code https://github.com/hkpeaks/peaks-framework/tree/main/PeaksDataFrameViewer
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Need Golang Community to Support a Hyper-performance of DataFrame Library
My Peaks DataFrame project is aiming at billion-row level data processing (extract-transform-load) for csv/parquet/json/excel/html files with little memory (recommend 16GB or above, for billion rows processing, it is recommended using NVMe SSD harddisk).
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Compare Golang Speed with C#, Golang, Pandas and Polars
Source code of basic programming can be download from github.com/hkpeaks/peaks-framework
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When I wanted to leave C#, I had two choices.
Source Code: github.com/hkpeaks/peaks-framework/tree/main/CompareProgrammingLanguage
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?
PeaksDataFrameViewer - Peaks DataFrame Viewer (previously known as youFast Desktop) is an HTML5 pivot table that supports fast and responsive viewing of transactions in summary view and pivot view. Both views allow you to drill down from summary figures to transaction level.
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 🚀
omniparser - omniparser: a native Golang ETL streaming parser and transform library for CSV, JSON, XML, EDI, text, etc.
modin - Modin: Scale your Pandas workflows by changing a single line of code
glibc - GNU Libc
datafusion - Apache DataFusion SQL Query Engine
peaks-consolidation - The Peaks Consolidation is equipped with state-of-the-art algorithms and data structures that support high-performance databending exercises. It specializes in management accounting and consolidation, with some special topics in machine learning and bioinformatics.
DataFrames.jl - In-memory tabular data in Julia
datatable - A Python package for manipulating 2-dimensional tabular data structures
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
db-benchmark - reproducible benchmark of database-like ops
rust-numpy - PyO3-based Rust bindings of the NumPy C-API