Rath VS ydata-profiling

Compare Rath vs ydata-profiling and see what are their differences.

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Rath ydata-profiling
43 43
3,965 12,022
2.7% 1.5%
7.1 8.5
13 days ago 5 days ago
TypeScript Python
GNU Affero General Public License v3.0 MIT License
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.

Rath

Posts with mentions or reviews of Rath. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-15.

ydata-profiling

Posts with mentions or reviews of ydata-profiling. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-26.

What are some alternatives?

When comparing Rath and ydata-profiling you can also consider the following projects:

superset - Apache Superset is a Data Visualization and Data Exploration Platform

dtale - Visualizer for pandas data structures

graphic-walker - An open source alternative to Tableau. Embeddable visual analytic

DataProfiler - What's in your data? Extract schema, statistics and entities from datasets

pygwalker - PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis

dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

PaLM-rlhf-pytorch - Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

lux - Automatically visualize your pandas dataframe via a single print! 📊 💡

get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

starcoder - Home of StarCoder: fine-tuning & inference!

evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b