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  3. Asabeneh
  4. 30-Days-Of-Python
PythonVisible projectFresh activityAdopted in packagesPartial snapshot

Asabeneh/30-Days-Of-Python

Rank not captured·Top 100·All-time stars·◐Cached ranking snapshot·Updated Oct 26, 2026

The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

Compare closest alternativesOpen GitHub
Stars
77.2K
Forks
13.9K
Daily momentum
—
Weekly momentum
—
30-day momentum
—

First read

Strong enough to justify a deeper source review

Asabeneh/30-Days-Of-Python looks visible enough to matter, active enough to trust for a next pass, and adopted enough to merit checking maintainers, releases, and real integration cost in the source repository.

Visible

77.2K public stars in the current GitStar snapshot.

Recognizable in the ecosystem

Active

Last commit Sep 30, 2026.

Fresh activity

Adopted

pypi gives the strongest production-style signal.

7.7M/week

Confidence

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

Partial snapshot

Snapshot facts

  • 77.2K stars
  • 13.9K forks
  • Last commit Sep 30, 2026
  • pypi 7.7M/week

Compare lens

donnemartin/data-science-ipython-notebooks and sinaptik-ai/pandas-ai 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
Sep 30, 2026

Package reality

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

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

Validation note

GitStar can summarize public signals for Asabeneh/30-Days-Of-Python, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

PyPI: 7.7M/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

pandas

7.7M downloads/week
pip install pandas
Open pypi
30-days-of-pythondatadata-sciencedatabaseflaskfullstackgithubherokumatplotlibmlmongodbnumpypandaspythonpython3
🐙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 fresh update.

Package adoptionFresh updateStable visibility
Momentum & Adoption Signals
Approximate star trajectory

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

Now: 77.2K
77.2K30d ago77.2K7d ago77.2K1d ago77.2KNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix
Current stars77.2K
Weekly package downloads7.7M

Bar lengths use a log scale so stars and package downloads stay comparable.

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 · 7.7M/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

donnemartin/data-science-ipython-notebooks

Shares the data category footprint with Asabeneh/30-Days-Of-Python, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

sinaptik-ai/pandas-ai

Shares the data category footprint with Asabeneh/30-Days-Of-Python, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

mwaskom/seaborn

Shares the data category footprint with Asabeneh/30-Days-Of-Python, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

apache/superset

Shares the data category footprint with Asabeneh/30-Days-Of-Python, 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 data-science-ipython-notebooks + pandas-ai as the closest next comparison from the related repository set.

Open compare presetCompare with data-science-ipython-notebooksCompare with pandas-ai
Repo utility

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GitHub RepositoryAsabeneh/30-Days-Of-PythonPyPI PackagepandasExport Ranking DataDownload CSV

Wider nearby ecosystem

Compare with data-science-ipython-notebooksCompare with pandas-ai
Python
29.4K
donnemartin/data-science-ipython-notebooks

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.

Python
23.9K
sinaptik-ai/pandas-ai

Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.

Python
14.1K
mwaskom/seaborn

Statistical data visualization in Python

Python
75.3K
apache/superset

Apache Superset is a Data Visualization and Data Exploration Platform

Python
50.3K
pandas-dev/pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Python
29K
reflex-dev/reflex

🕸️ Web apps in pure Python 🐍

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 Asabeneh/30-Days-Of-Python 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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