If you want to learn Python for data work, Jake VanderPlas’s Python Data Science Handbook is the closest match to a data-science book: its full text is free online as Jupyter notebooks, and it covers tools including NumPy, pandas, Matplotlib, and scikit-learn. If you are new to programming, start with Think Python or Python for Everybody instead; both introduce Python before you take on the data-science stack.
Which free Python book should you start with?
Choose based on what you already know and what you want Python to do. These books serve different starting points rather than forming a single set of interchangeable data-science textbooks.
| Book | Best starting point | Focus | Practice format and access |
|---|---|---|---|
| Think Python, third edition | New to programming | General programming concepts, introduced in sequence | Free online book with chapter Jupyter notebooks that can run on Google Colab |
| Python for Everybody | Learning Python through practical, data-related problems | Informatics and using Python to solve data-analysis problems | Free PDF, HTML, and EPUB editions |
| Python Data Science Handbook | Ready to work with Python data-science libraries | IPython, NumPy, pandas, Matplotlib, and scikit-learn | Full text in online Jupyter notebooks; the project also points to Colab and Binder |
Start with Think Python if programming itself is new
Green Tea Press describes the third edition as suitable for beginners and presents programming ideas in sequence. Its notebook chapters offer a way to work through examples rather than only reading static text. The book and notebook access are available from the Think Python third edition page.
Choose Python for Everybody for an informatics-oriented introduction
Python for Everybody connects learning Python with informatics and data-analysis problems. Its official book page lists free PDF, HTML, and EPUB formats, making it a flexible option if you prefer to read offline or on an e-reader. See the Python for Everybody book page.
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Move to the Handbook for the Python data stack
Python Data Science Handbook is aimed at practical work with commonly used Python data tools, not at teaching programming from the very beginning. Its online text is organized as Jupyter notebooks. The project repository links to the notebooks and hosted ways to run them.
What the Python Data Science Handbook covers
The handbook introduces a connected set of tools used in Python data work:
- IPython: an interactive environment for working with Python.
- NumPy: numerical computing and array operations.
- pandas: working with structured data.
- Matplotlib: plotting and visualization.
- scikit-learn: machine-learning methods.
That scope makes the book useful once you can read and write basic Python and are ready to learn the libraries. If terms such as variables, loops, and functions are still unfamiliar, build those foundations with one of the introductory books first.
How to access and work through the books
Read or run the Handbook notebooks
- Open the Python Data Science Handbook repository and navigate to its notebook files.
- Read the notebooks online, or use the repository’s Colab or Binder links to work with hosted notebook environments where available.
- Work through the examples in sequence and adapt them to your own data only after you understand what each step does.
The repository README says the book was written and tested with Python 3.5. That describes the book’s development environment, not a guarantee that its dependencies or every example will work unchanged in a current Python installation. If an old notebook fails, check the error and the current documentation for the relevant library rather than assuming your setup is wrong.
Use Think Python’s chapter notebooks
Open the third-edition book through Green Tea Press, then use its chapter notebook links to practice. The publisher says those notebooks can run on Colab, so local Python installation is not required for that hosted route.
Choose a reading format for Python for Everybody
Use the official page to select PDF, HTML, or EPUB. The listed formats let you read in a browser, save a document, or use an e-reader; the book’s focus remains an introduction to informatics and data-analysis problem solving.
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Is the printed Handbook required?
No. The complete online text is available free in the project’s notebooks. The repository also points to an optional printed edition through O’Reilly for readers who prefer a physical book; buying print is not necessary to access the online material.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check licenses before reusing material
Free access does not mean these books share the same reuse terms. The license can differ by book and, for the Handbook, by component. If you plan to copy, adapt, redistribute, or use material commercially, check the license for the exact edition and material you intend to use.
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- Python Data Science Handbook: the site states that the text is CC-BY-NC-ND and the code is MIT licensed.
- Think Python, third edition: CC BY-NC-SA 4.0.
- Python for Everybody: CC BY 4.0.
Consult the relevant book’s official page or repository for the applicable terms: Handbook project, Think Python third edition, and Python for Everybody.
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