Name | Version | Summary | date |
badgers |
0.0.10 |
Badgers: bad data generators |
2024-12-20 21:10:36 |
pointblank |
0.2.1 |
Find out if your data is what you think it is. |
2024-12-20 05:35:01 |
framework3 |
1.0.8 |
A flexible framework for machine learning pipelines |
2024-12-19 15:38:59 |
scitbx |
0.0.83 |
For academic data processing and plotting etc. |
2024-12-19 10:02:39 |
oracle-ads |
2.12.9 |
Oracle Accelerated Data Science SDK |
2024-12-18 19:10:55 |
scikit-posthocs |
0.11.2 |
Statistical post-hoc analysis and outlier detection algorithms |
2024-12-18 12:59:42 |
upgini |
1.2.28 |
Intelligent data search & enrichment for Machine Learning |
2024-12-18 10:41:44 |
causalexplain |
0.5.3 |
A package to extract the causal graph from continuous tabular data. |
2024-12-18 09:41:07 |
django-flow-forge |
0.9.9 |
Keep Data Ops and Machine Learning Ops (MLOps) simple and vendor agnostic with this Django module for defining, running and monitoring data work flows. |
2024-12-16 22:26:35 |
pythresh |
0.3.8 |
A Python Toolbox for Outlier Detection Thresholding |
2024-12-16 05:06:42 |
hirundo |
0.1.9 |
This package is used to interface with Hirundo's platform. It provides a simple API to optimize your ML datasets. |
2024-12-15 14:24:43 |
kedro-mlflow |
0.13.4 |
A kedro-plugin to use mlflow in your kedro projects |
2024-12-14 20:13:21 |
scikit-learn-intelex |
2025.0.1 |
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application. |
2024-12-12 12:05:46 |
graphbook |
0.9.3 |
The AI-drive data pipeline and workflow framework for data scientists and machine learning engineers. |
2024-12-11 18:41:04 |
statology |
0.0.1.dev0 |
Collection of Statistical Function for Data Science and Analytics |
2024-12-11 14:07:36 |
scikit-plots |
0.4.0.post0 |
An intuitive library to add plotting functionality to scikit-learn objects. |
2024-12-08 08:25:09 |
pyvespa |
0.51.0 |
Python API for vespa.ai |
2024-12-06 08:06:40 |
langagent |
2.1.6 |
LangAgent is a versatile multi-agent system designed to streamline complex tasks such as research, automated code generation, logical reasoning, data analysis, and dynamic reporting. Powered by advanced language models, it integrates seamlessly with external APIs, databases, and a variety of data formats, enabling developers and professionals to automate workflows, extract insights, and generate professional-grade reports effortlessly. |
2024-12-06 08:03:36 |
pyTigerGraph |
1.8.3 |
Library to connect to TigerGraph databases |
2024-12-05 01:46:47 |
daindex |
0.5.2 |
Deterioration Allocation Index Framework |
2024-12-04 18:45:01 |