Asabeneh/30-Days-Of-Python
Rank not captured·Top 100·All-time stars·Cached ranking snapshot·
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
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
73.4K public stars in the current GitStar snapshot.
Recognizable in the ecosystem
Active
Last commit Sep 10, 2026.
Fresh activity
Adopted
pypi gives the strongest production-style signal.
7.3M/week
Confidence
One or more key signals are partial, so GitStar keeps the interpretation conservative.
Partial snapshot
Snapshot facts
- 73.4K stars
- 13.4K forks
- Last commit Sep 10, 2026
- pypi 7.3M/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 10, 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.
Package signal
Install & Package Signals
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
7.3M downloads/week
pip install pandas
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: 73.4K
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars73.4K
Weekly package downloads7.3M
🧭 Hype vs Reality
BalancedRecent 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 · 7.3M/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.
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.
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.
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.
Related alternative
tangyudi/Ai-Learn
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.
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.
Repo utility
Embed Badge
Preview:
Markdown
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<a href="https://gitstar.space/repo/Asabeneh/30-Days-Of-Python"><img src="https://gitstar.space/api/badge/Asabeneh/30-Days-Of-Python" alt="GitStar"></a>Styles:
Wider nearby ecosystem
Python
29.3K starsdonnemartin/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
23.8K starssinaptik-ai/pandas-ai
Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
Python
14K starsmwaskom/seaborn
Statistical data visualization in Python
Unknown
13.3K starstangyudi/Ai-Learn
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
Python
74.7K starsapache/superset
Apache Superset is a Data Visualization and Data Exploration Platform
Python
49.7K starspandas-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
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.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
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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