RRepoGEO

REPOGEO REPORT · LITE

krzjoa/awesome-python-data-science

Default branch master · commit 6612257f · scanned 5/20/2026, 5:08:54 PM

GitHub: 3,433 stars · 441 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface krzjoa/awesome-python-data-science, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumtopics#1
    Add more specific "awesome list" and resource-related topics

    Why:

    CURRENT
    awesome, awesome-list, awesome-python, data-analysis, data-science, data-visualization, deep-learning, machine-learning, python, scikit-learn, statistics
    COPY-PASTE FIX
    awesome, awesome-list, awesome-python, data-analysis, data-science, data-visualization, deep-learning, machine-learning, python, scikit-learn, statistics, curated-list, python-resources, ml-resources, dl-resources
  • lowreadme#2
    Add a concise "What problem does this solve?" statement

    Why:

    COPY-PASTE FIX
    Add this text immediately after the main description blockquote:

## What problem does this solve?
This repository provides a single, comprehensive, and highly curated entry point for discovering the best Python libraries and tools across the entire data science ecosystem, from machine learning and deep learning to data manipulation and visualization.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface krzjoa/awesome-python-data-science
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pandas-dev/pandas
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pandas-dev/pandas · recommended 1×
  2. numpy/numpy · recommended 1×
  3. matplotlib/matplotlib · recommended 1×
  4. mwaskom/seaborn · recommended 1×
  5. scikit-learn/scikit-learn · recommended 1×
  • CATEGORY QUERY
    What are the best Python libraries for general-purpose data science tasks and analysis?
    you: not recommended
    AI recommended (in order):
    1. Pandas (pandas-dev/pandas)
    2. NumPy (numpy/numpy)
    3. Matplotlib (matplotlib/matplotlib)
    4. Seaborn (mwaskom/seaborn)
    5. Scikit-learn (scikit-learn/scikit-learn)
    6. SciPy (scipy/scipy)
    7. Plotly (plotly/plotly.py)

    AI recommended 7 alternatives but never named krzjoa/awesome-python-data-science. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive collection of Python tools for machine learning and deep learning?
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. TensorFlow
    3. PyTorch
    4. Keras
    5. Hugging Face Transformers
    6. XGBoost
    7. LightGBM
    8. CatBoost
    9. Pandas

    AI recommended 9 alternatives but never named krzjoa/awesome-python-data-science. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of krzjoa/awesome-python-data-science?
    pass
    AI did not name krzjoa/awesome-python-data-science — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts krzjoa/awesome-python-data-science in production, what risks or prerequisites should they evaluate first?
    pass
    AI named krzjoa/awesome-python-data-science explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo krzjoa/awesome-python-data-science solve, and who is the primary audience?
    pass
    AI did not name krzjoa/awesome-python-data-science — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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krzjoa/awesome-python-data-science — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite