3 Repos
Instructional materials that demonstrate how to use plotting libraries to create visual data representations.
Distinct from Python Visualization: Distinct from Python Visualization: provides instructional guides rather than the visualization libraries themselves.
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Dieses Projekt bietet eine Sammlung von PDF-Referenzleitfäden und visuellen Zusammenfassungen für die Matplotlib-Plotting-Bibliothek. Diese Leitfäden zur Datenvisualisierung kombinieren Codebeispiele und visuelle Hilfsmittel, um komprimierte technische Dokumentationen und Cheat-Sheets zu erstellen. Die Dokumentation wird durch eine LaTeX-basierte Kompilierungspipeline erstellt. Dieses System transformiert strukturierten Quellcode in hochwertig formatierte PDFs unter Verwendung einer mehrspaltigen Layout-Engine und skriptgesteuerter Asset-Generierung für Abbildungen. Der Build-Prozess umfasst eine automatisierte Asset-Pipeline, die die Auflösung von Schriftartabhängigkeiten handhabt und generierte Dateien auf Seitenzahlen und Link-Integrität validiert.
Provides concise visual aids and code examples for creating charts and figures in Python.
This project is a Python data science curriculum and programming tutorial collection. It provides a structured set of educational notebooks and scripts designed to teach data analysis, machine learning, and deep learning. The repository serves as a learning path for building and tuning predictive models, including regression, decision trees, and neural networks. It includes a data visualization guide for creating financial time-series plots and a multiprocessing reference for implementing parallel task execution and shared memory synchronization. The curriculum covers broader capability area
Includes detailed instructions and examples for creating and exporting financial time-series charts.
FriendsDontLetFriends is a scientific data visualization guide and framework designed to help users create accurate plots while avoiding common data representation mistakes. It provides a collection of scripts and guidelines for selecting distribution plots, color scales, and layouts that accurately represent complex experimental data. The project distinguishes itself through specialized toolkits for revealing hidden patterns in large datasets. It includes systems for heatmap optimization via dimension reordering and outlier management, as well as spatial layout algorithms to improve the inte
Provides instructional guides and curated examples to identify and avoid common data visualization mistakes.