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MazzaWill/neo4j-python-pandas-py2neo-v3

Default branch master · commit 2e2b08d3 · scanned 6/10/2026, 9:46:45 PM

GitHub: 579 stars · 187 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
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 MazzaWill/neo4j-python-pandas-py2neo-v3, 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

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

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening to highlight modern GraphRAG/vector search use cases

    Why:

    CURRENT
    # Excel to Neo4j Knowledge Graph

[](https://github.com/MazzaWill/neo4j-python-pandas-py2neo-v3/releases)
[](LICENSE)
[](https://github.com/MazzaWill/neo4j-python-pandas-py2neo-v3/issues/23)

English | [Simplified Chinese](README.zh-CN.md)

Excel-to-Neo4j examples for building knowledge graphs: a legacy py2neo v3 path for the original educational project, plus a modern Neo4j GraphRAG/vector-search path for current applications.
    COPY-PASTE FIX
    # Excel to Neo4j Knowledge Graph

**Build knowledge graphs from spreadsheet data for modern GraphRAG and vector search applications, while also preserving a legacy py2neo v3 example.**

[](https://github.com/MazzaWill/neo4j-python-pandas-py2neo-v3/releases)
[](LICENSE)
[](https://github.com/MazzaWill/neo4j-python-pandas-py2neo-v3/issues/23)

English | [Simplified Chinese](README.zh-CN.md)

Excel-to-Neo4j examples for building knowledge graphs: a legacy py2neo v3 path for the original educational project, plus a modern Neo4j GraphRAG/vector-search path for current applications.
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://github.com/MazzaWill/neo4j-python-pandas-py2neo-v3
  • mediumreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

This project uniquely offers both a legacy `py2neo v3` example for historical learning and a modern path for current Neo4j GraphRAG and vector search applications. Unlike general graph libraries (e.g., NetworkX) or the official `neo4j` driver, this repository provides a complete, runnable example for transforming invoice-style tabular data directly into a Neo4j knowledge graph, specifically addressing both older and newer integration patterns.

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 MazzaWill/neo4j-python-pandas-py2neo-v3
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NetworkX
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NetworkX · recommended 2×
  2. Neo4j · recommended 2×
  3. pandas · recommended 1×
  4. RDFLib · recommended 1×
  5. PyOrient · recommended 1×
  • CATEGORY QUERY
    How to transform spreadsheet data into a knowledge graph using Python dataframes?
    you: not recommended
    AI recommended (in order):
    1. pandas
    2. NetworkX
    3. RDFLib
    4. PyOrient
    5. OrientDB
    6. Neo4j Python Driver
    7. Neo4j
    8. Graphistry

    AI recommended 8 alternatives but never named MazzaWill/neo4j-python-pandas-py2neo-v3. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good approaches for building a graph database from tabular data for RAG applications?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. TypeDB
    3. Amazon Neptune
    4. ArangoDB
    5. GraphDB
    6. DGL
    7. PyG
    8. NetworkX

    AI recommended 8 alternatives but never named MazzaWill/neo4j-python-pandas-py2neo-v3. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 MazzaWill/neo4j-python-pandas-py2neo-v3?
    pass
    AI named MazzaWill/neo4j-python-pandas-py2neo-v3 explicitly

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

  • If a team adopts MazzaWill/neo4j-python-pandas-py2neo-v3 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MazzaWill/neo4j-python-pandas-py2neo-v3 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 MazzaWill/neo4j-python-pandas-py2neo-v3 solve, and who is the primary audience?
    pass
    AI did not name MazzaWill/neo4j-python-pandas-py2neo-v3 — 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?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite