Awesome GitHub RepositoriesPython Visualization

Matplotlib is a Python data visualization library and 2D plotting engine used to generate publication-quality figures and charts from numerical data. It serves as a numerical graphics library and data visualization toolkit for mapping data to visual elements. The library provides capabilities for producing static, animated, and interactive visualizations. This includes creating high-resolution figures for professional documents, generating moving graphics to illustrate data evolution over time, and building dynamic plots for interactive data exploration. The toolkit supports scientific plott

Bokeh is a Python data visualization library and interactive plotting framework used to create high-performance graphics and data dashboards that render in web browsers. It serves as a tool for generating standalone HTML documents, embedded components for digital notebooks, and full-stack web applications powered by a Python backend. The project distinguishes itself through its ability to handle large or streaming datasets while maintaining smooth interactivity. It enables linked brushing across multiple views, allowing data selected in one plot to automatically highlight corresponding data i

pyecharts is a Python visualization library and wrapper for the Echarts JavaScript engine. It translates Python data and configurations into JSON specifications to generate interactive web-based charts and graphs. The library provides specialized capabilities for geographic data mapping using a comprehensive library of map assets to visualize spatial information. It also includes utilities to capture rasterized snapshots of rendered web visualizations for export as static image files. The tool supports rendering interactive plots directly within data science notebook environments and exporti

This project is an exploratory data analysis library and profiling tool for Pandas and Spark DataFrames. It automates the initial investigation of datasets by generating comprehensive descriptive analysis reports, statistical summaries, and data quality warnings. The system functions as a data quality profiler to detect missing values, duplicate rows, and type inconsistencies. It includes a dataset comparison tool for identifying structural and content shifts between different versions of the same data, as well as specialized tools for time-series analysis to calculate auto-correlation and se

Altair is a declarative data visualization library for Python based on the Vega-Lite grammar. It allows users to create statistical visualizations by mapping data fields to visual properties rather than writing imperative drawing code. The library focuses on interactive charting through a system of linked selections and filters that update multiple visualizations based on user input. It renders charts as JSON and HTML for display in web browsers and interactive notebooks. The project covers statistical data analysis and interactive data exploration, providing capabilities to export visuals a

Altair is a declarative data visualization library for Python that generates Vega-Lite specifications. It functions as a tool for mapping data to graphical marks using a high-level syntax, allowing users to describe the desired visual outcome instead of writing imperative drawing commands. The framework enables the creation of interactive charts and graphics, including linked views and filtered displays that respond to user input in real time. It supports the design of multi-view dashboards by combining visualizations into layered or faceted layouts. The library provides capabilities for sta

LearnPython is a programming tutorial consisting of a collection of practical code examples used to demonstrate Python language features and programming patterns. It serves as a comprehensive learning resource that implements core language concepts through functional code. The project provides specialized guides and samples covering several key domains. These include asynchronous network programming with event loops and coroutines, data visualization using numerical datasets for 2D and 3D plots, and web scraping for fetching content and automating login flows. It also features instructions on

This project is a comprehensive library of practical Python code examples and patterns. It provides a collection of scripts and snippets designed to demonstrate a wide range of programming tasks, from basic syntax to advanced implementation patterns. The repository focuses on several core domains, including the implementation of concurrency and multithreading examples, data analysis snippets for cleaning and manipulating tabular data, and various data visualization examples. It also covers automation scripts for file system management and a variety of general programming patterns. Additional

Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin

Livebook is an interactive notebook platform for Elixir that provides a web-based environment for writing and running code cells alongside Markdown content, visualizations, and reproducible workflows. It serves as a multi-cloud auto-clustering tool that automatically discovers and joins Elixir nodes into clusters across Kubernetes, AWS ECS, and Fly.io for distributed execution, and also functions as a notebook deployment tool that packages notebooks into standalone web applications with authentication, secrets, and Docker support. The platform enables real-time collaborative editing, synchron

Lux ist ein automatisiertes Tool zur explorativen Datenanalyse, das entwickelt wurde, um intelligente visuelle Darstellungen von pandas Dataframes zu generieren. Es identifiziert Muster und Trends, indem es optimale Diagrammtypen und Achsen-Mappings basierend auf den statistischen Attributen eines Datensatzes empfiehlt. Das Tool fungiert als interaktive Datenprofilierungsschicht, die es Benutzern ermöglicht, Sammlungen von Diagrammen mithilfe von Filtern und Platzhaltern zu durchsuchen und abzufragen. Es dient zudem als Visualisierungs-Code-Generator, der automatisch erstellte Diagramme in programmatischen Code oder HTML zur manuellen Verfeinerung in externen Bibliotheken übersetzt. Das System deckt ein breites Spektrum an explorativen Analysefunktionen ab, einschließlich automatisierter Diagramm-Kodierung, geführter Entdeckung durch Schritt-Empfehlungen und der Möglichkeit, visuelle Konfigurationen als deklarative Spezifikationen zu exportieren. Dieses Projekt integriert sich direkt in pandas, um das Standard-Dataframe-Drucken durch interaktive Visualisierungskomponenten zu überschreiben.

Polynote ist eine polyglotte Notebook-Umgebung und ein interaktives Dokumentensystem, das für die Ausführung von Code in mehreren Sprachen innerhalb eines einzigen Dokuments entwickelt wurde. Es fungiert als sprachübergreifendes Datenanalysetool und JVM-Sprach-IDE, das es Nutzern ermöglicht, ausführbaren Code, Rich Text und Datenvisualisierungen zu kombinieren, um technische Workflows zu prototypisieren und zu dokumentieren. Das System zeichnet sich durch die Fähigkeit aus, Daten und Variablen zwischen verschiedenen Sprach-Runtimes, wie Python und der JVM, zu teilen. Es verwendet sprachübergreifende Objektkonvertierung und Data-Wrapping, um Objekte zwischen Runtimes zu übergeben und so mehrsprachige Daten-Workflows zu ermöglichen. Zudem lässt es sich in Apache Spark integrieren, um verteilte Datenverarbeitungsaufgaben über lokale oder Remote-Cluster-Submissions auszuführen. Die Plattform bietet eine umfassende Suite an Funktionen für Datenanalyse und -visualisierung, einschließlich einer Echtzeit-Symboltabelle für das Runtime-Monitoring und Unterstützung für das Rendern von Vega-Spezifikationen. Sie verwaltet Abhängigkeiten für JVM- und Python-Runtimes mittels koordinatenbasierter Auflösung und bietet IDE-erweitertes Editieren mit Autocomplete und Fehlerhervorhebung. Zu den Dokumentenverwaltungsfunktionen gehören ein dynamisches Inhaltsverzeichnis, eine notebookübergreifende Inhaltssuche und eine browserbasierte Backup-Wiederherstellung, um Datenverlust durch beschädigte Dateien zu verhindern.

missingno ist eine Python-Bibliothek zur Visualisierung und Analyse von Mustern fehlender Daten. Sie bietet eine Reihe von Tools, um die Vollständigkeit von Datensätzen zu profilieren, Datenlücken abzubilden und das Volumen von Null-Werten über Variablen hinweg zu quantifizieren. Die Bibliothek zeichnet sich durch einen Nullity-Korrelations-Analyzer und ein hierarchisches Daten-Clustering-Tool aus. Diese Komponenten ermöglichen die Erkennung systemischer Abhängigkeiten und Trends, indem gemessen wird, wie das Fehlen einer Variable mit dem Fehlen einer anderen zusammenhängt. Das Toolset deckt breitere Funktionen für Data-Quality-Auditing und explorative Analysen ab. Es enthält Features zur Zusammenfassung der Spalten-Nullität mittels linearer und logarithmischer Skalen sowie matrixbasierte Mappings zur Identifizierung systemischer Lücken in Datensätzen.

PyVista is a scientific 3D plotting framework and visualization library that provides a Python interface for rendering and analyzing spatial datasets using a VTK backend. It functions as a volumetric rendering engine and a 3D mesh analysis tool for computing geometric properties and performing boolean operations on surface and volumetric meshes. The project is distinguished by its ability to operate as a headless 3D renderer, generating high-quality renders and animations on remote servers without a physical display. It also features a lazy-accessor extension mechanism that allows the registr

ggpy is a Python library for statistical data visualization based on the grammar of graphics. It functions as a declarative framework for building complex charts by mapping data variables to visual properties through a structured coordinate system. The library enables the construction of composite visualizations by layering geometric shapes and statistical summaries. It utilizes a system of continuous and discrete scales to translate raw data into visual attributes and supports facet-based plotting to segment a single visualization into a grid of subplots based on variable categories. Visual

Python library that makes it easy for data scientists to create charts.

Python+Numpy+OpenGL: fast, scalable and beautiful scientific visualization

The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.

Text mode diagrams using UTF-8 characters and fancy colors

The power of Chart.js with Python