REPOGEO REPORT · LITE

buckaroo-data/buckaroo

Default branch main · commit efc83797 · scanned 6/1/2026, 3:36:57 PM

GitHub: 680 stars · 16 forks

AI VISIBILITY SCORE

40 /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

3 / 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 buckaroo-data/buckaroo, 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

hightopics#1

Add more descriptive topics to improve category signaling

Why:

CURRENT

buckaroo, data-science, jupyter, marimo-notebook, paddy, pandas, polars

COPY-PASTE FIX

buckaroo, data-science, jupyter, marimo-notebook, pandas, polars, dataframe, interactive, table-ui, data-exploration, eda, widget

mediumreadme#2

Refine the README's opening paragraph for immediate category clarity

Why:

CURRENT

Buckaroo is a modern data table for Jupyter that expedites the most common exploratory data analysis tasks. The most basic data analysis task - looking at the raw data, is cumbersome with the existing pandas tooling. Buckaroo starts with a modern performant data table that is sortable, has value formatting, and scrolls infinitely. On top of the core table experience, extra features like summary stats, histograms, smart sampling, auto-cleaning, and a low code UI are added.

COPY-PASTE FIX

Buckaroo is the interactive data table UI for Jupyter, designed to expedite common exploratory data analysis tasks. It provides a modern, performant table for Pandas and Polars DataFrames, featuring sorting, value formatting, infinite scrolling, summary statistics, histograms, smart sampling, auto-cleaning, and a low-code UI.

lowcomparison#3

Add a comparison section to the README

Why:

COPY-PASTE FIX

Add a new section to the README, e.g., '## Buckaroo vs. Other Tools', that briefly outlines how Buckaroo compares to PandasGUI, Qgrid, D-Tale, or other interactive dataframe tools, highlighting its unique strengths like code generation or specific UI features.

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 buckaroo-data/buckaroo

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

adamerose/pandasgui

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

adamerose/pandasgui · recommended 1×
quantopian/qgrid · recommended 1×
widgetti/ipyaggrid · recommended 1×
mwouts/itables · recommended 1×
DataTables.js · recommended 1×

CATEGORY QUERY
Seeking an interactive data table UI for quick exploratory data analysis in Jupyter notebooks.
you: not recommended
AI recommended (in order):
1. PandasGUI (adamerose/pandasgui)
2. Qgrid (quantopian/qgrid)
3. ipyaggrid (widgetti/ipyaggrid)
4. ITables (mwouts/itables)
5. DataTables.js
AI recommended 5 alternatives but never named buckaroo-data/buckaroo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools provide enhanced interactive dataframe exploration with sorting, searching, and summary statistics in notebooks?
you: not recommended
AI recommended (in order):
1. Pandas Profiling
2. Sweetviz
3. DataPrep.EDA
4. Lux
5. D-Tale
6. Qgrid
7. ITables
AI recommended 7 alternatives but never named buckaroo-data/buckaroo. 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 buckaroo-data/buckaroo?
pass
AI named buckaroo-data/buckaroo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts buckaroo-data/buckaroo in production, what risks or prerequisites should they evaluate first?
pass
AI named buckaroo-data/buckaroo 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 buckaroo-data/buckaroo solve, and who is the primary audience?
pass
AI named buckaroo-data/buckaroo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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