| Name | Version | Summary | date |
| SQLDataModel |
0.7.4 |
SQLDataModel is a lightweight dataframe library designed for efficient data extraction, transformation, and loading (ETL) across various sources and destinations, providing an efficient alternative to common setups like pandas, numpy, and sqlalchemy while also providing additional features without the overhead of external dependencies. |
2024-06-14 00:15:50 |
| mat-classification |
0.1b2 |
MAT-classification: Analysis and Classification methods for Multiple Aspect Trajectory Data Mining |
2024-06-10 16:39:36 |
| skfolio |
0.2.2 |
Portfolio optimization built on top of scikit-learn |
2024-06-04 22:50:13 |
| aeon |
0.9.0 |
A toolkit for machine learning from time series |
2024-05-31 20:12:11 |
| skpro |
2.3.1 |
A unified framework for probability distributions and probabilistic supervised regression |
2024-05-26 06:33:56 |
| shogunfolio |
0.1.0 |
Portfolio optimization built on top of scikit-learn |
2024-05-25 17:55:11 |
| mat-model |
0.1b5 |
MAT-model: Model Classes for Multiple Aspect Trajectory Data Mining |
2024-05-24 19:02:32 |
| mat-data |
0.1rc5 |
MAT-data: Data Preprocessing for Multiple Aspect Trajectory Data Mining |
2024-05-24 18:59:36 |
| skforecast |
0.12.0 |
Forecasting time series with scikit-learn regressors. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...). |
2024-05-05 12:52:38 |
| mat-clustering |
0.1b0 |
Clustering Methods for Multiple Aspect Trajectory Data Mining |
2024-04-12 17:24:12 |
| mat-similarity |
0.1b0 |
Similarity Methods and Functions for Multiple Aspect Trajectory Data Mining |
2024-04-12 17:23:14 |