HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
maxhumber
redframes
Python2022–2026
maxhumber / redframes

redframes

General Purpose Data Manipulation Library

6Distinct people
11Mentions
0Explicit recommenders
325GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

General Purpose Data Manipulation Library

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

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 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:04:58 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 year11 dated mentions

Observed mentions: 2022-09-19 19:14:47 UTC to 2026-05-22 13:02:59 UTC.

012342022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 4 mentions; 2 submissions, 2 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments. Incomplete calendar period.2026*

* 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.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC422
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC312
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC202
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC110

What Hacker News says

2 sources
In my opinion, Julia has the best alternative to dplyr in its Dataframes.jl package [1]. The syntax is slightly more verbose than dplyr because it's more explicit, but in exchange you get data transformations that you can leave for 6 months and when you come back you can read and understand very quickly. When I used R, if I hadn't commented a pipeline properly I would have to focus for a few minutes to understand it. In terms of performance, DF.jl seems to outperform dplyr in benchmarks, but for day to day use I haven't noticed much difference since switching to Julia. There are also APIs built on top of DF.jl, but I prefer using the functions directly. The most promising seems to be Tidier.jl [2] which is a recreation of the Tidyverse in Julia. In Python, Pandas is still the leader, but its API is a mess. I think most data scientists hav
Read the rest
en't used R, and so they don't know what they're missing out on. There was the Redframes project [3] to give Pandas a dplyr-esque API which I liked, but it's not being actively developed. I hope Polars can keep making progress in replacing Pandas, but it's still not quite as good as dplyr or even DF.jl. For plotting, Julia's time to first plot has got a lot better in recent versions, from memory it's something like 20 seconds a few years ago down to 3 seconds now. It'll never be as fast as matplotlib, but if you leave your terminal window open you only pay that price once. I actually think the best thing to come out of Julia recently is AlgebraOfGraphics.jl [4]. To me it's genuinely the biggest improvement to plotting since ggplot which is a high bar. It takes the ggplot concept of layers applied with the + operator and turns it into an equation, where + adds a layer on top of another, and the * operator has the distributive property, so you can write an expression like data * (layer_1 + layer_2) to visualise the same data with two visualisations. It's very powerful, but because it re-uses concepts from maths that you're already familiar with, it doesn't take a lot of brain space compared to other packages I've used. [1] https://dataframes.juliadata.org/ [2] https://github.com/TidierOrg/Tidier.jl [3] https://github.com/maxhumber/redframes [4] https://aog.makie.org/
In Big Book of R

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.