paddymul

buckaroo

HISTORICAL RECORD2023–2026

paddymul / buckaroo · Not publicly available

buckaroo

Discover this repository through its Hacker News conversations.

Explore on GitHub Read Hacker News discussions

2Distinct people

27Mentions

1Explicit recommenders

REPOSITORY AT A GLANCE

What to know before exploring

What it is

A public description has not been verified.

Where it is used

No documented product use has been verified yet.

What people are saying

I have a tool [1] that is tackling some of the same problems in a different way. I had some core views that shaped what I built. 1. When doing data manipulation, especially…

paddy_m used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year27 dated mentions

Observed mentions: 2023-05-17 14:46:33 UTC to 2026-10-01 19:04:06 UTC.

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	6	2	4
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	9	0	9
2025	2025-01-01 00:00:00 UTC	2026-01-01 00:00:00 UTC	10	1	9
2026 (partial)	2026-01-01 00:00:00 UTC	2027-01-01 00:00:00 UTC	2	0	2

What Hacker News says

27 sources

paddy_m·Oct 1, 2026

Location: Boston, MA Remote: Yes (preferred) Willing to relocate: No Technologies: Python, TypeScript, Rust, SQL | pandas, polars, Arrow, DuckDB/DataFusion, Parquet, PostgreSQL | React, ag-grid, FastAPI, Django | MCP servers, LLM tool design, agent evals Email: paddy@paddymullen.com I build data tools, from the dataframe backend to the browser. I created and maintain Buckaroo (https://github.com/paddymul/buckaroo), an open source dataframe UI for Jupyter, marimo, and VSCode — summary stats and a histogram over every column, across pandas and polars. 683 stars. Scrolling millions of rows without eating the browser took a segment-merging row cache and parquet decoded client side. Recently: Tallyman, a data science environment for coding agents. Named computations in a dependency DAG, results rendered live in a browser companion, every step

Read the rest

keeping the prompt that made it. Demoed at PyData London 2026. Before that: Flatfile, Attune, Tudor, and Bokeh/Wakari at Anaconda. Looking for data infrastructure, developer tools, or agent tooling — where a hard backend problem has to surface as something a person can use.

In Ask HN: Who wants to be hired? (October 2026)

paddy_m·Jun 1, 2026

Location: Boston Remote: Yes Relocate: Yes Technologies: Python, Pandas/Polars, NumPy, Jupyter, JS/TS, React Résumé: linkedin.com/in/paddymullen Email: paddy@paddymullen.com Python/Jupyter developer in Boston. I build data tools and I start by talking to the people who'll use them, because code that doesn't get adopted is wasted work. My main thing is Buckaroo (github.com/paddymul/buckaroo, ~680 stars), an open-source data table for Jupyter over Pandas/Polars. I built both the data layer and the React frontend. Looking for a team building data tooling (as a product or in house).

In Ask HN: Who wants to be hired? (June 2026)

paddy_m·Nov 3, 2025

Location: Boston Remote: Yes Willing to relocate: Yes Technologies: Python, Pandas/Numpy, Jupyter, JS/TS and something many devs skip: actually talking to users. Résumé/CV: https://www.linkedin.com/in/paddymullen/ Email: paddy@paddymullen.com I'm a developer who believes code isn't valuable unless it's used. That's why I start with conversations, not commits. I work to understand real problems before reaching for shiny new packages. I build tools that are simple, effective, and easy to adopt. My goal is always to solve the problem and make sure people know there’s a solution. In my next role, I'm looking to work with a team that values clarity and impact. I'm especially interested in data heavy environments where thoughtful tooling can make workflows better. Most recently, I built [Buckaroo](https://github.com/paddymul/buckaroo) an open s

Read the rest

ource data table for Jupyter using Pandas/Polars. It combines fast rendering, summary stats, and a low-code UI. It scratches an itch I've had for over a decade and has already streamlined my own analysis workflow.

In Ask HN: Who wants to be hired? (November 2025)

paddy_m·Aug 1, 2025

Read the rest

In Ask HN: Who wants to be hired? (August 2025)

paddy_m·Jul 1, 2025

Read the rest

In Ask HN: Who wants to be hired? (July 2025)

paddy_m·Jun 2, 2025

Location: Boston Remote: Yes Willing to relocate: Yes Technologies: talking to users, python, pandas/numpy, jupyter, js/ts Résumé/CV: https://www.linkedin.com/in/paddymullen/ Email: paddy@paddymullen.com In my next role, I want a broad mandate to make a meaningful impact within an organization by developing products that address genuine business challenges, with a preference for data related problems. Recently I built the data table for Jupyter/Pandas/Polars that I have wanted for over a decade. The open source Buckaroo (https://github.com/paddymul/buckaroo) data table combines a performant table, summary statistics, and a low code UI to expedite common data analysis tasks.

In Ask HN: Who wants to be hired? (June 2025)

paddy_m·May 18, 2025Submission

Show HN: Buckaroo – Data table UI for Notebooks

Buckaroo is my open source project. It is a dataframe viewer that has the basic features we expect in a modern table - scroll, search, sort. In addition there are summary stats, and histograms available. Buckaroo support Pandas and Polars dataframes and works on Jupter, Marimo, VSCode and Google Colab notebooks. All of this is extensible. I think of Buckaroo as a framework for building table UIs, and an initial data exploration app built on top of that framework. AG-Grid is used for the core table display and it has been customized with a declarative layer so you don't have to pass JS functions around for customizations. On the python side there is a framework for adding summary stats (with a small DAG for dependencies). There is also an entire Low Code UI for point and click selection of common commands (drop column). The lowcode UI also

Read the rest

generates a python function that accomplishes the same tasks. This is built on top of JLisp - a small lisp interpreter that reads JSON flavored lisp. Auto Cleaning looks at columns and heuristically suggests common cleaning operations. The operations are added to the lowcode UI where they can be edited. Multiple cleaning strategies can be applied and the best fit retained. Autocleaning without a UI and multiple strategies is very opaque. Since this runs heuristically (not with an LLM), it’s fast and data stays local. I'm eager to hear feedback from data scientists and other users of dataframes/notebooks.

105 points · Open discussion

paddy_m·May 14, 2025

Ok, if we are all tagging and promoting our own projects, check out mine. I created Buckaroo to provide a better table viewing experience inside of notebooks. I also built a low code UI and auto cleaning to expedite the wrote data cleaning tasks that take up a large portion of data analysis. Autocleaning is heuristically powered - no LLMs, so it's fast and your data stays local. You can apply different autocleaning strategies and visually inspect the results. When you are happy with the cleaning, you can copy and paste the python code as a reusable function. All of this is open source, and its extendable/customizable. Here's a video walking through autocleaning and how to extend it https://youtu.be/A-GKVsqTLMI Here's the repo: https://github.com/paddymul/buckaroo

In Show HN: CSV GB+ by Data.olllo – Open and Process CSVs Locally

paddy_m·May 9, 2025

This looks like many LLM assisted data projects which help and are flexible, but aren't repeatable and aren't fast enough to be interactive. Nao is a good execution of the concept. I built Buckaroo as a data table UI for Jupyter and Pandas/Polars, that first lets you look at the data in a modern performant table with histograms, formatting, and summary stats. Yesterday I released autocleaning for Buckaroo. This looks at data and heuristically chooses cleaning methods with definite code. This is fast (less than 500ms). Multiple cleaning strategies can be cycled through and you can choose the best approach for your data. For the simple problems we shoudn't need to consult an LLM to do the obvious things. All of this is open source and extensible. [1] https://youtube.com/shorts/4Jz-Wgf3YDc [2] https://github.com/paddymul/buckaroo [3] https:

Read the rest

//marimo.io/p/@paddy-mullen/buckaroo-auto-cleaning Live WASM notebook that you can play with - no downloads or installs required

In Launch HN: Nao Labs (YC X25) – Cursor for Data

paddy_m·May 8, 2025

I call it Trader UI vs VC UI. Traders want as much info jammed onto the screen as possible. VC backed companies use bootstrap, and want rounded corners on a pricing page with little actual information. I have been working on Buckaroo, a table UI for dataframes that runs in jupyter notebooks. It's much more TraderUI, with sparkline size histograms, and decent baked in formatting for numeric columns. https://github.com/paddymul/buckaroo

In Ask HN: What are good high-information density UIs (screenshots, apps, sites)?

paddy_m·Mar 3, 2025

In Ask HN: Who wants to be hired? (March 2025)

paddy_m·Feb 24, 2025

I am building Buckaroo [1], the data table UI in Jupyter that I have always wanted. I know how to use df.head(), df.describe(), sort, and run histograms on columns. I just got tired of typing 5 bits of code to minimally inspect each dataframe every time I looked at it. So I built buckaroo, it combines a high performance scrollable table (built on top of ag-grid), with summary stats, and histograms. All of this is customizable and extensible. I recently built a dataframe compare tool [2] on top of buckaroo that uses coloring to show differences between dataframes intuitively. Get in touch if you want to talk tables, data science tooling, or exploratory data analysis. [1] https://github.com/paddymul/buckaroo [2] https://www.youtube.com/shorts/u3PW6q36ufo

In Ask HN: What are you working on? (February 2025)

paddy_m·Nov 2, 2024

Curious to hear about other people's experience with Tanstack. I built a complex app [1] around AG-Grid and here are my thoughts: I would recommend AG-Grid for most data apps until a well supported alternative comes out. My understanding is that Tanstack Table is aimed at generic apps that happen to have a table. Perspective's [2] table component is most likely more capable than AG-Grids, but it needs significantly more documentation and examples. AG-Grid has a particular way of building things that seems to map to well to pulling data from a database or basic webservice. AG-Grid works very well for this use case. The AG-Grid enterprise features are built around this type of data model too. If your data is small enough to fit into memory (less than 50 MB), it works very well. AG-Grid doesn't have a connector to arrow or parquet (I wi

Read the rest

ll eventually write my own). For larger datasets JSON generation and parsing performance bottlenecks. Additionally Parquet/Arrow offers much better types. Configuring AG-Grid is tricky, especially when you want config to be declarative. If you want to define your own custom rendering components, getting them to work properly per column when you want different renderers for pinned rows is tricky (my solution [3]) Support for the community version of AG-Grid is practically non-existent. I understand that they have an enterprise business model that charges for support. They don't seem to be interested in answering community bug reports or comments, even for generically relevant bugs. I have a decent amount of trust in AG-Grid's community offering [4], I don't see them cannibalizing community features to put them in their enterprise offering. Their enterprise features are the main development focus of the team, particularly the charts. The charts offering isn't impressive compared to bokeh, plotly, or vega. It would work for line of business apps, but not for a userbase familiar with better analytic platforms. All that said. AG-Grid has excellent documentation and examples. They examples are swappable between Javascript, Typescript, React, and Angular. [1] https://github.com/paddymul/buckaroo [2] https://github.com/finos/perspective [3] live example https://buckaroo-data.readthedocs.io/en/latest/examples/#/DF... [4] https://blog.ag-grid.com/javascript-jabber-podcast/

In Tell HN: We (Causal) got acquired – thank you HN

paddy_m·Oct 22, 2024Recommendation phrase

I have a tool [1] that is tackling some of the same problems in a different way. I had some core views that shaped what I built. 1. When doing data manipulation, especially initial exploration and cleaning, we type the same things over and over. Being proficient with pandas involves a lot of recognition of patterns, and hopefully remembering one with well written code (like you would read in Effective Pandas). 2. pandas/polars is a huge surface space in terms of API calls, but rarely are all of those calls relevant. There are distinct operations you would want on a datetime column, a string column or an int column. The traditional IDE paraidgm is a bit lacking for this type of use (python typing doesn't seem to utilize the dtype of a column, so you see 400 methods for every column). 3.It is less important for a tool to have the right a

Read the rest

nswer out of the box, vs letting you cycle through different views and transforms quickly. ------ I built a low code UI for Buckaroo that has a DSL (JSON Lisp) that mostly specifies transform, column name, and other arguments. These operations are then applied to a dataframe, and separately the python code is generated from templates for each command. I also have a facility for auto-cleaning that heuristically inspects columns and outputs the same operations. So if a column has 95% numbers and 1% blank strings, that should probably be treated as a numeric column. These operations are then visible in the lowcode UI. Multiple cleaning methods can be tried out (with different thresholds). [1] https://github.com/paddymul/buckaroo [2] https://youtu.be/GPl6_9n31NE?si=YNZkpDBvov1lUYe4&t=603 Demonstrating the low code UI and autocleaning in about 3 minutes [3] There are other related tools in this space, specifically visidata and dtale. They take different approaches which are worth learning from. ps: I love this product space and I'm eager to talk to anyone building products in this area.

In Show HN: Data Formulator – AI-powered data visualization from Microsoft Research

paddy_m·Oct 16, 2024

> Lisp needs braces You're a troll, but I'll feed you. I adapted Peter Norvig's excellent lispy2.py [0] to read json. I call it JLisp [1]. Lispy2 is a scheme implementation, complete with macros that executes on top of python. I made it read json, really just replacing () with []. and defining symbols as {'symbol': 'symbol_name'}. I built it because it's easier to get a webapp to emit JSON then paren lisp. I also knew that building an interpreter on top of lisp meant that I wouldn't back myself into a corner. There is incredible power in the lisp, especially the ability to transform code. [0] https://norvig.com/lispy2.html [1] https://github.com/paddymul/buckaroo/blob/main/tests/unit/li... #tests for JLisp

In Hofstadter on Lisp (1983)

Showing the 15 most recent of 27 sources. Enable JavaScript to search and browse every indexed mention.