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jakevdp
PythonDataScienceHandbook
Jupyter Notebook2016–2025
jakevdp / PythonDataScienceHandbook

PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

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7Distinct people
8Mentions
0Explicit recommenders
50.4KGitHub stars
REPOSITORY AT A GLANCE

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What it is

Python Data Science Handbook: full text in Jupyter Notebooks

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year8 dated mentions

Observed mentions: 2016-09-10 08:10:53 UTC to 2025-12-02 16:58:00 UTC.

01232016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC110
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC211
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC110
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC321

What Hacker News says

8 sources
phone_book·Dec 2, 2025
The linked Github seems to have the 2nd edition in the form of notebooks, https://github.com/jakevdp/PythonDataScienceHandbook/blob/ma..., under the Using Code Examples section, "attribution usually includes the title, author, publisher, and ISBN. For example: "Python Data Science Handbook, 2nd edition, by Jake VanderPlas (O’Reilly). Copyright 2023..." compared to the OP's link which has "The Python Data Science Handbook by Jake VanderPlas (O’Reilly). Copyright 2016..."
In Python Data Science Handbook
mscolnick·Jan 14, 2025Submission

Show HN: WASM-powered codespaces for Python notebooks on GitHub

Hi HN! Last year, we shared marimo [1], an open-source reactive notebook for Python with support for execution through WebAssembly [2]. We wanted to share something new: you can now run marimo and Jupyter notebooks directly from GitHub in a Wasm-powered, codespace-like environment. What makes this powerful is that we mount the GitHub repository's contents as a filesystem in the notebook, making it really easy to share notebooks with data. All you need to do is prepend 'marimo.app' to any Python notebook on GitHub. Some examples: - Jupyter Notebook: https://marimo.app/github.com/jakevdp/PythonDataScienceHandb... - marimo notebook: https://marimo.app/github.com/marimo-team/marimo/blob/07e8d1... Jupyter notebooks are automatically converted into marimo notebooks using basic static analysis and source code transformations. Our conversion logi
Read the rest
c assumes the notebook was meant to be run top-down, which is usually but not always true [3]. It can convert many notebooks, but there are still some edge cases. We implemented the filesystem mount using our own FUSE-like adapter that links the GitHub repository’s contents to the Python filesystem, leveraging Emscripten’s filesystem API. The file tree is loaded on startup to avoid waterfall requests when reading many directories deep, but loading the file contents is lazy. For example, when you write Python that looks like ```python with open("./data/cars.csv") as f: print(f.read()) # or import pandas as pd pd.read_csv("./data/cars.csv") ``` behind the scenes, you make a request [4] to https://raw.githubusercontent.com/<org>/<repo>/main/data/car.... Docs: https://docs.marimo.io/guides/publishing/playground/#open-no... [1] https://github.com/marimo-team/marimo [2] https://news.ycombinator.com/item?id=39552882 [3] https://blog.jetbrains.com/datalore/2020/12/17/we-downloaded... [4] We technically proxy it through the playground https://marimo.app to fix CORS issues and GitHub rate-limiting.
204 points · Open discussion
Foivos·Sep 16, 2019
It is possible to do the same things in Pandas is many different ways. It is good to have this flexibility, but it is confusing for new users. On top of that the bracket operator ([) is overloaded in many ways. Things started to make sense after I read a very good book on Pandas[1]. Reading a book is better than reading blog posts, because it is consistent. In contrast, reading small tutorials for every little thing is confusing, because every blog post is using a different way to do the same thing. [1] https://github.com/jakevdp/PythonDataScienceHandbook
In Comprehensive Guide on Data Visualization with Pandas

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