<p>In this tutorial, we explore the advanced capabilities of PyGWalker, a powerful tool for visual data analysis that integrates seamlessly with pandas. We begin by generating a realistic e-commerce dataset enriched with time, demographic, and marketing features to mimic real-world business data. We then prepare multiple analytical views, including daily sales, category performance, and customer [&#8230;]</p>
<p>The post <a href="https://www.marktechpost.com/2025/11/11/how-to-build-an-end-to-end-interactive-analytics-dashboard-using-pygwalker-features-for-insightful-data-exploration/">How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>

تم نشر دليل في 12 نوفمبر 2025 يوضح كيفية إنشاء لوحة تحكم تحليلية تفاعلية باستخدام PyGWalker، وهي أداة مصممة لتحليل البيانات بصريًا. يركز الدليل على مجموعة بيانات التجارة الإلكترونية التي تتضمن ميزات زمنية وديموغرافية وتسويقية، مما يسمح للمستخدمين باستكشاف وجهات نظر تحليلية متنوعة مثل المبيعات اليومية وأداء الفئات. تعتبر هذه المورد مهمًا لمحللي البيانات الذين يسعون لتحسين قدراتهم في استكشاف البيانات.

Se publicó un tutorial el 12 de noviembre de 2025, que detalla cómo crear un panel de análisis interactivo utilizando PyGWalker, una herramienta diseñada para el análisis visual de datos. El tutorial se centra en un conjunto de datos de comercio electrónico que incluye características temporales, demográficas y de marketing, permitiendo a los usuarios explorar diversas vistas analíticas como ventas diarias y rendimiento de categorías. Este recurso es significativo para los analistas de datos que buscan mejorar sus capacidades de exploración de datos.

Un tutoriel a été publié le 12 novembre 2025, détaillant comment créer un tableau de bord analytique interactif en utilisant PyGWalker, un outil conçu pour l'analyse visuelle des données. Le tutoriel se concentre sur un ensemble de données e-commerce comprenant des caractéristiques temporelles, démographiques et marketing, permettant aux utilisateurs d'explorer diverses vues analytiques telles que les ventes quotidiennes et la performance des catégories. Cette ressource est significative pour les analystes de données cherchant à améliorer leurs capacités d'exploration de données.

A tutorial was published on November 12, 2025, detailing how to create an interactive analytics dashboard using PyGWalker, a tool designed for visual data analysis. The tutorial focuses on an e-commerce dataset that includes time, demographic, and marketing features, allowing users to explore various analytical views such as daily sales and category performance. This resource is significant for data analysts seeking to enhance their data exploration capabilities.

How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration

arXiv:2601.08621v1 Announce Type: new 
Abstract: Recent advances in search-augmented large reasoning models (LRMs) enable the retrieval of external knowledge to reduce hallucinations in multistep reasoning. However, their ability to operate on graph-structured data, prevalent in domains such as e-commerce, social networks, and scientific citations, remains underexplored. Unlike plain text corpora, graphs encode rich topological signals that connect related entities and can serve as valuable priors for retrieval, enabling more targeted search and improved reasoning efficiency. Yet, effectively leveraging such structure poses unique challenges, including the difficulty of generating graph-expressive queries and ensuring reliable retrieval that balances structural and semantic relevance. To address this gap, we introduce GraphSearch, the first framework that extends search-augmented reasoning to graph learning, enabling zero-shot graph learning without task-specific fine-tuning. GraphSearch combines a Graph-aware Query Planner, which disentangles search space (e.g., 1-hop, multi-hop, or global neighbors) from semantic queries, with a Graph-aware Retriever, which constructs candidate sets based on topology and ranks them using a hybrid scoring function. We further instantiate two traversal modes: GraphSearch-R, which recursively expands neighborhoods hop by hop, and GraphSearch-F, which flexibly retrieves across local and global neighborhoods without hop constraints. Extensive experiments across diverse benchmarks show that GraphSearch achieves competitive or even superior performance compared to supervised graph learning methods, setting state-of-the-art results in zero-shot node classification and link prediction. These findings position GraphSearch as a flexible and generalizable paradigm for agentic reasoning over graphs.

تم تقديم إطار عمل جديد يسمى GraphSearch، الذي يوسع التفكير المعزز بالبحث إلى تعلم الرسوم البيانية، مما يمكّن التعلم من الرسوم البيانية بدون ضبط محدد للمهمة. هذا التقدم يعالج التحديات المتعلقة بالتعامل مع البيانات الهيكلية في الرسوم البيانية، والتي تزداد شيوعًا في مجالات متنوعة مثل التجارة الإلكترونية والشبكات الاجتماعية.

Se ha introducido un nuevo marco llamado GraphSearch, que extiende el razonamiento aumentado por búsqueda al aprendizaje de grafos, permitiendo el aprendizaje de grafos sin ajuste específico a la tarea. Este avance aborda los desafíos de operar con datos estructurados en grafos, que son cada vez más prevalentes en diversos dominios como el comercio electrónico y las redes sociales.

Un nouveau cadre nommé GraphSearch a été introduit, étendant le raisonnement augmenté par la recherche à l'apprentissage des graphes, permettant un apprentissage de graphes sans coupure spécifique à la tâche. Cette avancée répond aux défis liés à l'exploitation des données structurées en graphes, de plus en plus présentes dans divers domaines tels que le commerce électronique et les réseaux sociaux.

A new framework named GraphSearch has been introduced, extending search-augmented reasoning to graph learning, enabling zero-shot graph learning without the need for task-specific fine-tuning. This advancement addresses the challenges of operating on graph-structured data, which is increasingly prevalent in various domains such as e-commerce and social networks.

GraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learning

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How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration

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