hamilton
polars
hamilton | polars | |
---|---|---|
26 | 144 | |
878 | 26,378 | |
- | 3.4% | |
8.1 | 10.0 | |
about 1 year ago | 3 days ago | |
Python | Rust | |
BSD 3-clause Clear License | GNU General Public License v3.0 or later |
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hamilton
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Write production grade pandas (and other libraries!) with Hamilton
And find the repository here: https://github.com/dagworks-inc/hamilton/
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Useful libraries for data engineering in various programming languages
Python - https://github.com/stitchfix/hamilton (author here). It's great if you want your code to be always unit testable and documentation friendly, and you want to be able to visualize execution. Blog post on using it with Pandas https://link.medium.com/XhyYD9BAntb.
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Cognitive Loads in Programming
Yes! As one of the creators of https://github.com/stitchfix/hamilton this was one of the aims. Simplifying the cognitive burden for those developing and managing data transforms over the course of years, and in particular for ones they didn't write!
For example in Hamilton -- we force people to write "declarative functions" which then are stitched together to create a dataflow.
E.g. example function -- my guess is that you can read and understand/guess what it does very easily.
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Prefect vs other things question
For (1) there are quite a few options - prefect is one, metaflow is another, airflow, dagster, even https://github.com/stitchfix/hamilton (core contributor here), etc.
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Field Lineage
If you're want to do more python https://github.com/stitchfix/hamilton allows you to model dependencies at a columnar (field) level.
- Show HN
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[D] Is anyone working on interesting ML libraries and looking for contributors?
Take a look at https://github.com/stitchfix/hamilton - we're after contributors who can help us grow the project, e.g. make documentation great, dog fooding features and suggesting/contributing usability improvements.
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Useful Python decorators for Data Scientists
For a real world example of their power, we built an entire framework (https://github.com/stitchfix/hamilton) at Stitch Fix, where a lot of cool magic is provide via decorators - see https://hamilton-docs.gitbook.io/docs/reference/api-reference/available-decorators and these two source files (https://github.com/stitchfix/hamilton/blob/main/hamilton/function_modifiers_base.py, https://github.com/stitchfix/hamilton/blob/main/hamilton/function_modifiers.py ). Note we do some non-trivial stuff via them.
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unit tests
For data processing/transform code, I would recommend looking at https://github.com/stitchfix/hamilton, especially if you're trying to test pandas code. Short getting started here - https://towardsdatascience.com/how-to-use-hamilton-with-pandas-in-5-minutes-89f63e5af8f5 (disclaimer: I'm one of the authors).
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Dealing with hundreds of customer/computed columns
The python package, hamilton, from Stitch Fix (https://hamilton-docs.gitbook.io/docs/) can help manage transformations on pandas dataframes. This DAG of transformations is managed separately in a file - so it can be versioned, in case the transformations change. The memory required is reduced, because only the API call tables and mapping parameter table have to be in memory. The calculated columns can be produced as needed. Just like dbt, transformations are separate from the source tables - but hamilton can be used on any python object - not just dataframes. dbt is SQL based.
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?
prosto - Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
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 🚀
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.
modin - Modin: Scale your Pandas workflows by changing a single line of code
plumbing - Prismatic's Clojure(Script) utility belt
datafusion - Apache DataFusion SQL Query Engine
OpenLineage - An Open Standard for lineage metadata collection
DataFrames.jl - In-memory tabular data in Julia
composer - Supercharge Your Model Training
datatable - A Python package for manipulating 2-dimensional tabular data structures
codetour - VS Code extension that allows you to record and play back guided tours of codebases, directly within the editor.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing