xorbits
Prophet
xorbits | Prophet | |
---|---|---|
7 | 221 | |
1,011 | 17,767 | |
1.7% | 0.6% | |
8.8 | 6.2 | |
about 1 month ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
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
Prophet
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Moirai: A Time Series Foundation Model for Universal Forecasting
https://facebook.github.io/prophet/
"Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well."
- prophet: NEW Data - star count:17116.0
- prophet: NEW Data - star count:17082.0
- Facebook Prophet: library for generating forecasts from any time series data
- prophet: NEW Data - star count:16196.0
- prophet: NEW Data - star count:15889.0
What are some alternatives?
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.
tensorflow - An Open Source Machine Learning Framework for Everyone
darts - A python library for user-friendly forecasting and anomaly detection on time series.
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
scikit-learn - scikit-learn: machine learning in Python
PyBrain
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
trueskill - An implementation of the TrueSkill rating system for Python
greykite - A flexible, intuitive and fast forecasting library
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
MLflow - Open source platform for the machine learning lifecycle