Lmst

#tabularData

TabPFN-2.5 – SOTA foundation model for tabular data

https://priorlabs.ai/technical-reports/tabpfn-2-5-model-report

#HackerNews #TabPFN2.5 #SOTA #TabularData #FoundationModel #AIResearch

🚨 Breaking news: Java developers discover Pandas rip-off with a name that sounds like a cheap gym 💪. #Fahmatrix promises to make tabular data “fahm” easy, but let's be real—it's just another way to make Java developers wish they had chosen Python instead 🐍.
https://github.com/moustafa-nasr/fahmatrix #JavaDevelopers #PandasRipOff #TabularData #PythonVsJava #HackerNews #ngated

⭐️ What you’ll learn ⭐️

✅ Detecting and fixing errors in tables
• Learn to work with #tabulardata
• Don’t get lost when validating your spreadsheet
• Clean up your spreadsheets to gain valuable insights

👉🏾 More: https://buff.ly/9XVJxq4

🧵

Design patterns for presenting and manipulating tabular data.

"Datatable Design Patterns"
https://bootcamp.uxdesign.cc/data-table-design-patterns-4e38188a0981

#ui #datatables #designpatterns #tabulardata #hci #frontend

⭐️ What you’ll learn ⭐️

✅ Detecting and fixing errors in tables
• Learn to work with #tabulardata
• Don’t get lost when validating your spreadsheet
• Clean up your spreadsheets to gain valuable insights

👉🏾 More: https://blog.okfn.org/2025/03/25/announcing-our-new-course-quality-and-consistent-data-with-the-open-data-editor/

🧵

OK this still need some ironing but... in #LeagueCSV 9.22.0 a new interface is added `TabularData` which enables the following magic 😍 #PHP #interoperability #extension #tabulardata for more information and limitations check the documentation https://csv.thephpleague.com/9.0/reader/resultset/#instantiation

$The picture illustrate how you can user League\CSV package to directly interact with popular RDBMS system in PHP to turn any query result into a object processable by the package.$

When you're dealing with small datasets, Tablib is all you need. Eirik Berge, PhD explains everything you need to know about Tablib in his latest article. Read now!

#Python #TabularData

https://towardsdatascience.com/use-tablib-to-handle-simple-tabular-data-in-python-fa9e6f0af37f

#Design #Challenges
Invisible success · Showcasing a well-designed, accessible table component https://ilo.im/15yldl

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#Table #TabularData #Accessibility #Component #DesignSystem #UiDesign #WebDesign #Development #WebDev #Frontend

The Design Philosophy of Great Tables
https://posit-dev.github.io/great-tables/blog/design-philosophy/

#GreatTables #python #TabularData #Tables

#Design #Guides
The design philosophy of great tables · Principles and guidelines for designing impactful tables https://ilo.im/15yhdj

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#Table #TabularData #DesignPattern #ProductDesign #UxDesign #UiDesign #WebDesign #Development #WebDev #Python

I ran a quick Gradient Boosted Trees vs Neural Nets check using scikit-learn's dev branch which makes it more convenient to work with tabular datasets with mixed numerical and categorical features data (e.g. the Adult Census dataset).

Let's start with the GBRT model. It's now possible to reproduce the SOTA number of this dataset in a few lines of code 2 s (CV included) on my laptop.

1/n

#sklearn #PyData #MachineLearning #TabularData #GradientBoosting #DeepLearning #Python

Screenshot of syntax highlighted snippet from: https://nbviewer.org/github/ogrisel/notebooks/blob/master/sklearn_demos/gbdt_vs_neural_nets_on_tabular_data.ipynb

The GBDT cross-validated accuracy is 0.873 +/- 0.002.

Interpretable Graph Neural Networks for Tabular Data
https://arxiv.org/abs/2308.08945
Discussion: https://news.ycombinator.com/item?id=37269376

* GNN essentially deep NN black-box models
* IGNNet: Interpretable Graph Neural Network for tab data
* notable HN comment, resp. to critique: " Right, the significance of orig. article & related research is ChatGPT-like models don't handle tabular data well & there's need for things that do"

#DataProcessing #GraphNeuralNetworks #GNN #TabularData #GPT #LLM #IGNNet

Interpretable Graph Neural Networks for Tabular Data

Figure 1: An overview of our proposed approach. Each data instance is represented as a graph by embedding the feature values into a higher dimensionality, and the edge between two features (nodes) is the correlation value. Multiple iterations of message passing are then applied. Finally, the learned node representation is projected into a single value, and a whole graph representation is obtained by concatenating the projected values.

Figure 2: IGNNet default architecture. It starts with the embedding layer, a linear transformation from one dimension to 64 dimensions. A Relu activation function follows each message-passing layer and each green block as well. The feedforward network at the end has no activation functions between layers to ensure a linear transformation into a single value. A sigmoid activation function follows the feedforward network to obtain the final value for each feature between 0 and 1.

Article: https://arxiv.org/pdf/2308.08945.pdf

Discussion (Hacker News): https://news.ycombinator.com/item?id=37269376

#Development #Techniques
Tabular numbers · Number alignment with an underrated CSS property https://ilo.im/14qchr

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#WebDev #Frontend #CSS #NumberAlignment #TabularData #MovingNumbers #Readability

The Wildland Fire Trends Tool (WFTT) is a #datavisualization and analysis tool that calculates and displays #wildfire #trends and #patterns for the western U.S. based on user-defined regions of interest, time periods, and ecosystem types. Users can use the tool to easily generate a variety of #maps, #graphs, and #tabulardata products that are informative for all levels of expertise. https://www.usgs.gov/centers/forest-and-rangeland-ecosystem-science-center/science/wildland-fire-trends-tool