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  4. data-science-ipython-notebooks
PythonVisible projectStale activityAdopted in packagesPartial snapshot

donnemartin/data-science-ipython-notebooks

Rank not captured·Top 100·All-time stars·◐Cached ranking snapshot·Updated Aug 15, 2026

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Compare closest alternativesOpen GitHub

First read

Real package usage exists, but freshness needs a closer look

donnemartin/data-science-ipython-notebooks shows signs of reuse beyond stars, yet recent activity is softer. Read the repository as a potentially durable dependency, but verify maintenance rhythm before treating it as a default choice.

Visible

29.3K public stars in the current GitStar snapshot.

Recognizable in the ecosystem

Active

Last commit Mar 20, 2024.

Stale activity

Adopted

pypi gives the strongest production-style signal.

2.9M/week

Confidence

One or more key signals are partial, so GitStar keeps the interpretation conservative.

Partial snapshot

Snapshot facts

  • 29.3K stars
  • 8K forks
  • Last commit Mar 20, 2024
  • pypi 2.9M/week

Compare lens

keras-team/keras and streamlit/streamlit are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.

Signal trail

Trajectory

Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.

Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Mar 20, 2024

Package reality

GitStar found pypi:scipy. Package traffic can help separate visible repositories from dependencies that are quietly used in real workflows.

Hype vs Reality
Balanced
Package mapping
pypi · scipy
Cross-links
Standalone repo read

Validation note

GitStar can summarize public signals for donnemartin/data-science-ipython-notebooks, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

PyPI: 2.9M/week
Package signal

Install & Package Signals

📐 How this feeds Hype vs Reality

GitStar uses the strongest linked package signal below when it reads Hype vs Reality for this repository.

Balanced
Recent attention and package usage are moving in the same direction.
PyPI package

scipy

2.9M downloads/week
pip install scipy
Open pypi
awsbig-datacaffedata-sciencedeep-learninghadoopkagglekerasmachine-learningmapreducematplotlibnumpypandaspythonscikit-learnscipysparktensorflowtheano
🐙Polyglot
🛡️ Editorial Context

GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.

Why this rank

This repository stands out because it combines package adoption and stable visibility.

Package adoptionStable visibility
📈 Momentum & Adoption Signals
Approximate star trajectory

Reconstructed from current stars and cached daily/weekly/monthly deltas.

Now: 29.3K
30d ago7d ago1d agoNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars29.3K
Weekly package downloads2.9M
🧭 Hype vs Reality
Balanced

Recent attention and package usage are moving in the same direction.

This usually means the project has both mindshare and a measurable production footprint, which makes it a stronger validation candidate.

Status
Balanced
Package footprint
PyPI · 2.9M/week
Method note
Balanced means GitStar sees both meaningful package adoption and enough current star velocity or scale to avoid reading the repo as purely niche. Compare it against other Python repos before treating stars as a moat.
📐 Read the Hype vs Reality heuristic
Related alternative

keras-team/keras

Shares the learning category footprint with donnemartin/data-science-ipython-notebooks, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

streamlit/streamlit

Shares the learning category footprint with donnemartin/data-science-ipython-notebooks, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

ray-project/ray

Shares the learning category footprint with donnemartin/data-science-ipython-notebooks, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

gradio-app/gradio

Shares the learning category footprint with donnemartin/data-science-ipython-notebooks, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Research links

Cross-links

Compare against related repos

GitStar picked keras + streamlit as the closest next comparison from the related repository set.

Open compare presetCompare with kerasCompare with streamlit
Repo utility

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GitHub Repositorydonnemartin/data-science-ipython-notebooksPyPI PackagescipyExport Ranking DataDownload CSV

Wider nearby ecosystem

Compare with kerasCompare with streamlit
Python
64.2K stars
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43.5K stars
ray-project/ray

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

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gradio-app/gradio

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

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💫 Industrial-strength Natural Language Processing (NLP) in Python

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Lightning-AI/pytorch-lightning

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

Next step after the validation read

Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Compare the closest alternativesRead the heuristic

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

About This Page

This page provides a quick overview of donnemartin/data-science-ipython-notebooks based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.

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