skorch
datatable
| skorch | datatable | |
|---|---|---|
| 3 | 9 | |
| 5,648 | 1,790 | |
| 0.8% | 0.5% | |
| 6.9 | 6.1 | |
| 5 days ago | 6 months ago | |
| Jupyter Notebook | C++ | |
| BSD 3-clause "New" or "Revised" License | Mozilla Public 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.
skorch
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[P] skorch 0.12.0 - HuggingFace integrations for sklearn, M1 support and others
Find a detailled list of changes in the release text.
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[P] ray-skorch - distributed PyTorch on Ray with sklearn API
I'm the principal author of ray-skorch, a library that lets you run distributed PyTorch training on large-scale datasets while providing a familiar, scikit-learn compatible skorch API, integrating well with the rest of the scikit-learn ecosystem.
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Scikit-Learn Version 1.0
There are scikit-learn (sklearn) API-compatible wrappers for e.g. PyTorch and TensorFlow.
Skorch: https://github.com/skorch-dev/skorch
tf.keras.wrappers.scikit_learn: https://www.tensorflow.org/api_docs/python/tf/keras/wrappers...
datatable
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Cheat Sheets for data.table to Python's pandas syntax?
Aside from that, there is a Python translation of data.table (see documentation here), which might be worth looking into. However, it hasn't had any major updates in a while: the last release 2 years ago ...
- Any advice on using Pandas as a data analyst?
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Alternative to Pandas
There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.
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Need advice on whether to store data set for regression model in SQL database or by using Python modules like Pickle or Parquet
just use HDF5 or Parquet, or CSV + https://github.com/h2oai/datatable to speed up the file reading.
- Massive R analysis of Data Science Language and Job Trends 2022
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Scikit-Learn Version 1.0
> For me I had with pandas the most issues using it's multiindex.
Yessss. I loathe indices, and have never been in a situation where I was better off with them than without them.
> Regarding fast you have something like Vaex on python sid
I've never used Vaex, but I've used datatable (https://github.com/h2oai/datatable) and polars (https://github.com/pola-rs/polars). Polars is my favorite API, but datatable was faster at reading data (Polars was faster in execution). I'll have to give Vaex a try at some point.
- Show HN: Sheet2dict – simple Python XLSX/CSV reader/to dictionary converter
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Hey Reddit, here's my comprehensive course on Python Pandas, for free.
Yep. I think this is the downside to a package being entirely maintained by volunteers. In any case, Pandas is still the leading data wrangling package for Python. (I'm excited to see how datatable evolves.)
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Ditching Excel for Python in a Legacy Industry (Reinsurance)
h2o's data.table clone is fine
https://github.com/h2oai/datatable
What are some alternatives?
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
scikit-learn - scikit-learn: machine learning in Python
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
db-benchmark - reproducible benchmark of database-like ops
sktime - A unified framework for machine learning with time series
scientific-visualization-book - An open access book on scientific visualization using python and matplotlib
ray-skorch - Distributed skorch on Ray Train
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.