#datastructure — Public Fediverse posts

🎩✨ Behold the majestic skiplist: the data structure nobody asked for but everyone pretends to need! 🤡 This article bravely navigates a sea of #buzzwords and sales pitches, leaving readers wondering if the true purpose of skiplists is to skip the point entirely. 🚀
https://antithesis.com/blog/2026/skiptrees/ #skiplist #datastructure #techhumor #programming #satire #HackerNews #ngated

[New Blog Post] Alpha Equivalent Hash Consing with Thinnings https://www.philipzucker.com/thin_hash_cons_codebruijn/ #logic #datastructure @pigworker

My memory isn't getting worse, it's just using exponential decay - by Matthew Kim

https://chunkofcoal.com/posts/exponential-decay/

#WebAssembly #WASM #DataStructure #Algorithm #Algorithms #Rust #RustLang

Index, Count, Offset, Size

https://tigerbeetle.com/blog/2026-02-16-index-count-offset-size/

#HackerNews #Index #Count #Offset #Size #DataStructure #Programming

La matematica delle strutture dati racconta una verità semplice: non esiste un modo perfetto per archiviare informazioni.

Ogni sistema è un compromesso tra velocità, memoria e ordine. A volte serve struttura, altre volte un po’ di disordine funziona meglio. Un equilibrio dinamico, in cui l’efficienza nasce proprio dalla varietà delle soluzioni.

https://www.quantamagazine.org/why-theres-no-single-best-way-to-store-information-20260116/

#computerscience #datastructure #data #algorithm #algoritmi #informatica

Matching Algorithm with Recursively Implemented StorAge (MARISA) is a space-efficient, fairly fast, and static trie data structure. MARISA serves as a dictionary structure, and by definition, it supports exact match lookup, which is the basic operation of dictionary. In addition, MARISA supports reverse lookup, common prefix search, and predictive search.

Thanks to @terrtia for the discovery.

#datastructure #computerscience

🔗 https://www.s-yata.jp/marisa-trie/docs/readme.en.html

Grmbl.😩

Implementing an object graph with with loops.

After setup, it needs additional initialization.

The fields getting set will not change again. Ideally would be final (Java, or readonly in TypeScript). And I don't want an "if (bla!=null)" where ever access I them.

In my ideal solution this would be guarded by the type system where the nodes, once initialized, change type. I know roughly how it could be done, but its weird and cumbersome. 😕

#Java #final #datastructure #strongTyping

Hey Mastodon! 👋 I'm diving back into my DSA journey and want to move beyond just solving theoretical problems. I'm looking to build some real-world projects to apply what I'm learning.

Any project ideas where you can genuinely use DSA concepts? Things like a simple recommendation engine, a social network graph, or a pathfinding visualizer. I'm curious about how you would implement these.

#devcommunity #DSA #Projects #Programming #Tech #algorithm #datastructure

Visualize your Python data structures with just one click.
Hash Set: https://memory-graph.com/#codeurl=https://raw.githubusercontent.com/bterwijn/memory_graph/refs/heads/main/src/hash_set.py&breakpoints=32&continues=1&timestep=0.5&play

#python #programming #memory_graph #DataStructure

Visualize your Python data structure with just one click.
Linked List: https://memory-graph.com/#codeurl=https://raw.githubusercontent.com/bterwijn/memory_graph/refs/heads/main/src/linked_list.py&timestep=0.2&play

#Python #memory_graph #DataStructure

Understanding and debugging Data Structures is easier when you can see the structure of your data using memory_graph: https://github.com/bterwijn/memory_graph

Here we show values being added to a Linked List in Cursor AI. When adding the last value '5' we "Step Into" the code to show more of the details: https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/linked_list.gif

🎥 See the Quick Intro video for the VS Code integration: https://youtu.be/23_bHcr7hqo

#Python #LinkedList #DataStructure #memory_graph #debug

Understanding and debugging Data Structures is easier when you can see the structure of your data using memory_graph: https://github.com/bterwijn/memory_graph

In this example we show values being inserted in a Binary Tree. When inserting the last value '29' we "Step Into" the code to show the recursive implementation: https://shorturl.at/bx848

🎥 See the Quick Intro video for the VS Code integration: https://youtu.be/23_bHcr7hqo

#Python #BinaryTree #Tree #DataStructure #memory_graph #debug

Reflecting some more on the Sketchpad & ECS parts of this talk: SideFX Houdini organizes all geometry data in similar vertical silos of points, vertices, edges, faces, prims, each with component IDs, each with its own group of native and user-defined attribs, and with similar powerful "omniscient" visibility/access from anywhere. That structure makes VEX SOPs akin to "systems" in an ECS setup and the handling/scripting itself very fun & powerful. The GUI also provides spreadsheet views of the geometry (again similar to e.g. what FLECS provides for debugging). Considering the age of Houdini, I think this approach is notable...

Blender's BMesh Radial Mesh implementation[1] is more traditional OOP structured, but the core idea of "discs" (aka bi-directional circular lists) of pointers to vertices & edges now seems somewhat relevant to some Sketchpad ideas too. Also a reminder that I really need to find/make time to update & release my own mesh implementation (from 2018) combining ideas from both Houdini & BMesh... It's already been a year (again) since I last talked about & touched it... 😱

[1] https://developer.blender.org/docs/features/objects/mesh/bmesh/

#Blender #Houdini #Mesh #Geometry #DataStructure #ECS #Sketchpad

At first glance, bar charts might seem like a simple visualization type. But with a little creativity, they can be enhanced in countless ways to reveal deeper insights and make your data shine.

The attached visual highlights a variety of bar chart styles to inspire your work.

Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #data #tidyverse #rstats #package #datasciencetraining

I recently made a very popular LinkedIn post about Simpson's Paradox, which resulted in an engaging conversation. Paul Julian made a great comment on the relationship between Mixed Effects Models and Simpson's Paradox that I wanted to share with you.

In the plot below (generated from reproducible code – thanks, Paul!), you can see how different models compare:

Original post: https://www.linkedin.com/posts/joachim-schork_coding-bigdata-rprogramming-activity-7242865910405824512-oNmF/

Further details: http://eepurl.com/gH6myT

#datavisualization #database #datastructure #data

Using dplyr and ggplot2 in R can significantly streamline your data analysis process, making it easier to work with complex data sets.

I have created a video tutorial in collaboration with Albert Rapp, where I demonstrate how to do this in practice: https://www.youtube.com/watch?v=EKISB0gnue4

#coding #datavisualization #rprogramming #dataviz #statisticalanalysis #package #datastructure #ggplot2 #bigdata #tidyverse

🟪 Harnessing the Power of Data Structure to Build Resilient Power Apps

Too often, Power Apps makers jump into app building—without giving enough thought to data structure. In Ep. 55 of #PowerTalks, Griffin Lickfeldt from Citizen Developer explains why getting this step right from the start is critical for building scalable, secure, and AI-ready apps.

💡 The Superpower of Dataverse
🔍 Strategic Table Structuring
⚖️ Future-Proof Your Apps
🔒 Security as a Cornerstone

▶︎ https://www.hubsite365.com/en-ww/citizen-developer/?id=33ec151c-5941-f011-8779-002248826e75&topic=8daf8386-bb75-ea11-a811-000d3a210788&theater=true

#DataStructure #PowerApps #CopilotStudio #CitizenDeveloper

Mean imputation is a common method for handling missing values in numerical data. It replaces missing values with the mean of the observed values, ensuring the data set remains complete and easy to use.

The image below illustrates the impact of mean imputation. The black line represents the original data distribution before imputation, while the red line shows the data distribution after imputation.

Tutorial: https://statisticsglobe.com/mean-imputation-for-missing-data/

Newsletter: http://eepurl.com/gH6myT

#datastructure

🧵 Thread on sorting algorithms.

Starting with #StupidSort 🤪, an inefficient algorithm that sorts by randomly shuffling until the list is ordered.

(1/3) ⬇️

#computerscience #algorithm #sortingalgorithm #datastructure #coding #programming #software #softwaredevelopment #bigo #learntocode #codenewbie

Shoutout to my friend Daniel, who is not on mastodon. He created an uber efficient slice implementation for joining/modifying/copying lists at scale. The blogpost and github README describe the algorithm. It's an interesting technical read if you have 10-20 minutes of spare time.

https://daniel.avery.io/writing/fork-join-data-structures

#code #algorithm #datastructure #random

Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/

More details are available at this link: http://eepurl.com/gH6myT

#research #datastructure #businessanalyst #data

Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/

More details are available at this link: http://eepurl.com/gH6myT

#research #datastructure #businessanalyst #data

Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/

More details are available at this link: http://eepurl.com/gH6myT

#research #datastructure #businessanalyst #data

Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/

More details are available at this link: http://eepurl.com/gH6myT

#research #datastructure #businessanalyst #data

Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.

For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/

More details are available at this link: http://eepurl.com/gH6myT

#research #datastructure #businessanalyst #data

Designing an Efficient Tree Index on Disaggregated Memory

https://cacm.acm.org/research-highlights/designing-an-efficient-tree-index-on-disaggregated-memory/

#computing #datastructure #algorithm #software

gganimate is a powerful extension for ggplot2 that transforms static visualizations into dynamic animations. By adding a time dimension, it allows you to illustrate trends, changes, and patterns in your data more effectively.

The attached animated visualization, which I created with gganimate, showcases a ranked bar chart of the top 3 countries for each year based on inflation since 1980.

More information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #datavisualization #tidyverse #ggplot2

Visualizing gene structures in R? gggenes, an extension of ggplot2, simplifies the process of creating clear and informative gene diagrams, making genomic data easier to interpret and share.

Visualization: https://cran.r-project.org/web/packages/gggenes/vignettes/introduction-to-gggenes.html

Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

#datastructure #datavisualization #dataanalytics #data #tidyverse #datascientists #ggplot2