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Ranked by Attention Score: stars gained over the last 180 complete days

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Topics: learning-python

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2 repositories by Attention Score · May 7, 2026 – Nov 2, 2026 (UTC)

Topic: learning-python
License: Other license
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1.virgili0 / Virgilio

Your new Mentor for Data Science E-Learning.

tensorflow
machine-learning
machine-vision
datascience
scikit-learn
python
+14 more

Jupyter Notebook Other license15K total2.5K forks

+770 stars

from 14,236 · score 6.65

2.junnplus / awesome-python-books

:books: Directory of Python books

python-book
learning-python
book
python-cookbook
python-programming

Other license4.3K total686 forks

+90 stars

from 4,224 · score 4.51

Order: Attention Score

Most net stars gained in the window first. The score only puts this gain on a log scale, so the order is the order of the gain.

How the ranking works

Ranked by stars gained in the window. The score is the natural log of one plus that gain, so a repository's rank is exactly its star gain's rank.

score = log1p(net stars gained in the trailing T complete UTC days)

A repository is ranked only when its Star History covers every day of the window and its latest Observation is not missing.

The longest window is 180 days. Star History from the Repository Host goes back at most 180 days before a repository's first Observation. A longer window needs more days of Observations first. A repository younger than the window is not ranked in it.

The filters narrow the same eligible repositories. A repository matches the topics when it has any of them. Total stars is the count now. Last push and creation on the Repository Host count back from the start of today (UTC). The counts beside the languages, licenses, and topics include all ranked repositories in the window. A Tag holds the repositories that have it; see Tags.

Repositories enter the index from the Seed Dataset or by gaining stars faster than the published admission threshold, whatever their size. Admission is permanent, so an archived repository can still rank.

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