pgmpy VS rustworkx

Compare pgmpy vs rustworkx and see what are their differences.

pgmpy

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks. (by pgmpy)

rustworkx

A high performance Python graph library implemented in Rust. (by Qiskit)
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pgmpy rustworkx
2 4
2,617 829
1.4% 6.8%
8.0 9.1
6 days ago 11 days ago
Python Rust
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

pgmpy

Posts with mentions or reviews of pgmpy. We have used some of these posts to build our list of alternatives and similar projects.

rustworkx

Posts with mentions or reviews of rustworkx. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-08.

What are some alternatives?

When comparing pgmpy and rustworkx you can also consider the following projects:

causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.

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

statsmodels - Statsmodels: statistical modeling and econometrics in Python

Graphia - A visualisation tool for the creation and analysis of graphs

scikit-learn - scikit-learn: machine learning in Python

hathor-core - HathorNetwork's fullnode core

CausalPy - A Python package for causal inference in quasi-experimental settings

Factotum - A system to programmatically run data pipelines

pyhf - pure-Python HistFactory implementation with tensors and autodiff

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.

Lottery-Simulation - This program can simulate a number of drawings in the Lottery (6 out of 49). The guesses and the draws are chosen randomly and the user can choose how many right guesses there should be (0-6). Then the program will run through the simulation as many times as it takes to get the exact number of correct guesses the user chose. The user can also choose how many times this should be repeated (the higher the number, the more accurate the result will be). Then the program will automatically calculate the average number of tries it took to get the chosen number of correct guesses and tell the user the chance of getting this certain number of correct guesses.

hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.