best-of-ml-python VS ydata-profiling

Compare best-of-ml-python vs ydata-profiling and see what are their differences.

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best-of-ml-python ydata-profiling
16 43
15,335 12,053
1.5% 1.5%
7.8 8.5
2 days ago 4 days ago
Python Python
Creative Commons Attribution Share Alike 4.0 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.

best-of-ml-python

Posts with mentions or reviews of best-of-ml-python. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-10.

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 best-of-ml-python and ydata-profiling you can also consider the following projects:

Awesome-WAF - 🔥 Web-application firewalls (WAFs) from security standpoint.

dtale - Visualizer for pandas data structures

ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

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

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

ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)

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

awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.

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.

kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

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