dmatrix2np
xorbits
| dmatrix2np | xorbits | |
|---|---|---|
| 2 | 7 | |
| 17 | 1,024 | |
| - | 2.9% | |
| 0.0 | 8.8 | |
| over 2 years ago | about 2 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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dmatrix2np
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You can convert from DMatrix to NumPy array!
It's open-source so you can take a look at the code here: https://github.com/aporia-ai/dmatrix2np
xorbits
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Everything you need to know about pandas 2.0.0!
Here’s our project: https://github.com/xprobe-inc/xorbits
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Introducing Xorbits: A Distributed Python Data Science Framework for Large Dataset Analysis
Hey everyone, we are excited to announce our new project, Xorbits, a scalable data science framework that aims to scale the entire Python data science world.
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Use maximum PC Hardware Resources
My suggestion is to use some parallel computing framework like Xorbits. The framework will parallel your workload automatically. For data processing tasks, just use xorbits.pandas or xorbits.numpy, and you can run almost any python workload with xorbits.remote.
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Use "distributed pandas" to scale your data science workflow!
If you are interested in learning more about Xorbits, please visit our project's Github for more information: https://github.com/xprobe-inc/xorbits
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A new way to accelerate your data science workflow
Xorbits can be an ideal solution for these issues. Xorbits is a scalable Python data science framework that aims to scale the Python data science stack while keeping the API compatibility. You can get an out-of-box performance gain by changing `import pandas as pd` to `import xorbits.pandas as pd`.
- Scalable Python data science, in an API compatible and fast way
What are some alternatives?
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Data Flow Facilitator for Machine Learning (dffml) - The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
trueskill - An implementation of the TrueSkill rating system for Python
neptune-client - 📘 The MLOps stack component for experiment tracking
tensorflow - An Open Source Machine Learning Framework for Everyone
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
PyBrain
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
adaptive - :chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions