A Practical Introduction to PySpark Window Functions

PySpark for Beginners: Beyond the Basics

Take the next step to building real workflows with Spark on your laptop The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science .

PySpark for Beginners: Building Intermediate-Level Skills

A practical next step into partitions, shuffles, joins, caching, and execution plans. The post PySpark for Beginners: Building Intermediate-Level Skills appeared first on Towards Data Science .

Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet

Tabular foundation models predict the missing column of any spreadsheet zero-shot, the way an LLM completes text — and on the TabArena benchmark they now sit above fully tuned gradient-boosted trees. An introduction to how they work, an independent reproduction of the strongest open one, and a map of where XGBoost still wins.

The Problem with pandas Isn’t Performance. It’s Cognitive Overhead.

Faster dataframe engines are nice, but they don't reduce the amount of syntax an analyst has to hold in their head. The post The Problem with pandas Isn’t Performance.

Introducing Gradio's new Dataframe!

Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn

Exploratory data analysis on the US Census Dataset The post Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn appeared first on Towards Data Science .

Generalizing Fair Top-$k$ Selection: An Integrative Approach

arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.

Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

arXiv:2606. 30452v1 Announce Type: new Abstract: Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks.

Efficient Group Lasso Regularized Rank Regression with Simulation-Based Tuning

arXiv:2510. 11546v3 Announce Type: replace-cross Abstract: High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods.

Introducing GPT-5.3-Codex-Spark

Introducing GPT-5. 3-Codex-Spark—our first real-time coding model.

Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths

Tabular machine learning benchmarks typically summarize performance by averaging scores, ranks, or pairwise wins across datasets. Such aggregates are useful for selecting robust default models, but th...

Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets