60-Days-of-Data-Science-and-ML
hamilton
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60-Days-of-Data-Science-and-ML | hamilton | |
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5 | 26 | |
22 | 878 | |
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10.0 | 8.1 | |
over 1 year ago | about 1 year ago | |
Jupyter Notebook | Python | |
- | BSD 3-clause Clear License |
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60-Days-of-Data-Science-and-ML
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60 Days of Data Science and Machine Learning
Day 31 - Machine Learning Linear Regression
Followings are fourth week of this series. You can find them on my GitHub. You can run all the notebook on colab or jupyter notebook as well.
Day 15 - Repression Part2
Day 1 - Python Basics Part1
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.
What are some alternatives?
data-science-notes - Notes of IBM Data Science Professional Certificate Courses on Coursera
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
MAPIE - A scikit-learn-compatible module for estimating prediction intervals.
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.
eip1559_analysis - Can we estimate the economic impact of EIP-1559 on miners? This repository try to estimate the loss of miners' revenue coming from transactions fees, using Ethereum historical data.
plumbing - Prismatic's Clojure(Script) utility belt
collatz-conjecture - A calculator as Jupyter Lab notebook for Collatz Conjecture or commonly known as 3x+1 problem.
OpenLineage - An Open Standard for lineage metadata collection
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
composer - Supercharge Your Model Training
linear-regression-from-scratch - A data science project for part II physics project E (surveying using stars)
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust