ISL-python VS ydata-profiling

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

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ISL-python ydata-profiling
4 40
180 11,497
- 1.7%
0.0 8.4
over 1 year ago 4 days ago
Jupyter Notebook Python
- 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.


Posts with mentions or reviews of ISL-python. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-12.
  • ESL vs ISLR books?
    2 projects | /r/datascience | 12 Oct 2021
    Here or here for the Python versions of ISLR.
  • The Hundred-Page Machine Learning Book
    2 projects | | 25 Jan 2021
    I typically recommend a few different books to everyone who finishes the bootcamp, based on a self-assessment they take. I recommend some books based on their strengths, so they can find a career path sooner, and some books based on their weaknesses, so they can widen their cone of oppportunity within ML.

    In our consultancy, data science is done in Python and SQL (and PySpark, but I don't hand out books on that during bootcamp!), and ML delivery is a combination of math, software engineering, and architecture/product owner disciplines.

    If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.

    If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.

    If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.

    If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.

    If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)

    For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."

    Anyway, I've spent a lot of time reading and reviewing books about ML, and my key takeaway is ones that get you closer to writing actual code to solving actual problems for actual people are the ones to focus on.


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 2023-06-12.

What are some alternatives?

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

dtale - Visualizer for pandas data structures

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

ISLR-python - An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

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.

evidently - Evaluate and monitor ML models from validation to production. Join our Discord:

dataprep - Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

sweetviz - Visualize and compare datasets, target values and associations, with one line of code.

jupyterlab-lsp - Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol

lux - 👾 Fast and simple video download library and CLI tool written in Go

awesome-python - A curated list of awesome Python frameworks, libraries, software and resources