ydata-profiling VS awesome-python

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

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ydata-profiling awesome-python
43 85
11,992 204,062
1.3% -
8.5 7.1
7 days ago 6 days ago
Python Python
MIT License GNU General Public License v3.0 or later
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.

awesome-python

Posts with mentions or reviews of awesome-python. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-07.

What are some alternatives?

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

dtale - Visualizer for pandas data structures

Qtile-Config - This is my configuration of Qtile, a window manager written in python.

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

VeRyPy - A python library with implementations of 15 classical heuristics for the capacitated vehicle routing problem.

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

Pyadomd - A pythonic approach to query SSAS data models.

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

DearPyGui - Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies

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.

Box - Python dictionaries with advanced dot notation access

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

timebudget - Stupidly-simple speed measurements for Python.