iterative-stratification
Dask
| iterative-stratification | Dask | |
|---|---|---|
| 1 | 32 | |
| 817 | 12,022 | |
| - | 0.8% | |
| 0.0 | 9.6 | |
| almost 2 years ago | 2 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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iterative-stratification
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TypeError: unhashable type: 'list' when preparing index of labels for MultiLabelBinarizer
I need to create this so I can encode the Labels and run iterative stratification as detailed [here](https://github.com/trent-b/iterative-stratification). Once I have the index prepared, i will run MultiLabelBinarizer to encode the "Labels" list and create a matrix of those values. I will then run the stratification sampling algorithm on that matrix to determine zero-based train and test indices. The code I have below is causing an error.
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
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Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
timebasedcv - Time based splits for cross validation
NetworkX - Network Analysis in Python
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
blaze - NumPy and Pandas interface to Big Data