ydata-profiling VS best-of-ml-python

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

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ydata-profiling best-of-ml-python
43 16
12,022 15,302
1.5% 1.3%
8.5 7.9
6 days ago 8 days ago
Python Python
MIT License Creative Commons Attribution Share Alike 4.0
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.

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.

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.

What are some alternatives?

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

dtale - Visualizer for pandas data structures

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

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

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

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

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

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

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.

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

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

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