AI Cheatsheets vs Python Data Science Handbook
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A collection of downloadable cheat sheet images and PDFs for machine learning and data tools.
Open source · MIT
The full text of a Python data science book, delivered as runnable Jupyter notebooks.
The short answer
Choose AI Cheatsheets if you need…
- Students who want a one-page summary of a library's main functions
- Practitioners who need a quick reminder of syntax while coding
- People browsing an overview of which ML and data tools exist
- Instructors looking for printable reference material
Choose Python Data Science Handbook if you need…
- Students learning NumPy, Pandas, Matplotlib, or Scikit-Learn through hands-on examples
- Analysts who already know Python and want a practical tour of data science libraries
- Readers who prefer running code next to the text rather than only reading
- People who want to try the notebooks without local setup, using Colab or Binder
Side-by-side
| AI Cheatsheets | Python Data Science Handbook | |
|---|---|---|
| Type | Learning resource | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 15.4k | 50.3k |
| Setup difficulty | Beginner | Beginner |
| How you get it | Read it online | Clone and build from source |
| Main language | — | Jupyter Notebook |
| Last code update | 7 years ago | 2 years ago |
What each one is
AI Cheatsheets
This repository is a set of quick-reference cheat sheets covering common deep learning, machine learning, and data tools such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, PySpark, and Dask. Each sheet is a one-page visual summary of commands and concepts, provided as images and PDFs. It is meant as a handy reference while you work or study, not as a full course or software library.
Python Data Science Handbook
This repository holds the complete Python Data Science Handbook as Jupyter notebooks, so the explanations and the code examples live together. It introduces core Python data libraries such as IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. You can read it online, or run the notebooks yourself to try the code.
Best for
AI Cheatsheets
- Students who want a one-page summary of a library's main functions
- Practitioners who need a quick reminder of syntax while coding
- People browsing an overview of which ML and data tools exist
- Instructors looking for printable reference material
Python Data Science Handbook
- Students learning NumPy, Pandas, Matplotlib, or Scikit-Learn through hands-on examples
- Analysts who already know Python and want a practical tour of data science libraries
- Readers who prefer running code next to the text rather than only reading
- People who want to try the notebooks without local setup, using Colab or Binder
Strengths
AI Cheatsheets
- Covers many widely used tools in one place
- Free to use under the MIT License
- Sheets are available as both images and PDFs for easy viewing or printing
Python Data Science Handbook
- Text and runnable code are combined in the same notebooks
- Free to read online, with Colab and Binder links for running code
- Code is released under the MIT license
Not ideal for
AI Cheatsheets
- Complete beginners who need step-by-step teaching, since the sheets assume some familiarity with each tool
- Anyone needing up-to-date API details, because the last push was in October 2019 and newer library versions may differ
- People looking for runnable code or a software package, since this is a set of static reference files
Python Data Science Handbook
- Complete beginners to Python: the README says familiarity with Python is assumed, and points to a separate companion project for that
- Readers who need current library versions: the book was tested with Python 3.5, and the README warns some pinned package versions may not be available on every platform
Trade-offs
AI Cheatsheets
- Last updated in October 2019, so some content may be out of date
- The README does not describe how the sheets are kept current or who maintains them
- Reference material only; it does not teach concepts in depth
Python Data Science Handbook
- Written and tested with Python 3.5; newer library versions may behave differently, and the README admits some pinned versions may be unavailable
- The text is licensed CC-BY-NC-ND, which limits reuse and modification of the book content, unlike the MIT-licensed code
Getting started
AI Cheatsheets
Clone the repository
git clone https://github.com/kailashahirwar/cheatsheets-aiPython Data Science Handbook
Install requirements into the current conda environment
conda install --file requirements.txtCreate a stand-alone environment named PDSH with Python 3.5
conda create -n PDSH python=3.5 --file requirements.txtFrequently asked questions
What is the difference between AI Cheatsheets and Python Data Science Handbook?
AI Cheatsheets: A collection of downloadable cheat sheet images and PDFs for machine learning and data tools. Python Data Science Handbook: The full text of a Python data science book, delivered as runnable Jupyter notebooks. AI Cheatsheets suits students who want a one-page summary of a library's main functions, while Python Data Science Handbook suits students learning numpy, pandas, matplotlib, or scikit-learn through hands-on examples.
Are AI Cheatsheets and Python Data Science Handbook free?
AI Cheatsheets: Free and open source. Python Data Science Handbook: Free and open source.
Is AI Cheatsheets or Python Data Science Handbook more popular?
Python Data Science Handbook has more GitHub stars (50.3k) than AI Cheatsheets (15.4k). Popularity isn't the same as fit — check which one matches your use case above.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.