Awesome GitHub RepositoriesPlot Axis Customizers

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

Plotly.js is a JavaScript charting library and interactive graphing framework used to create web-based visualizations. It functions as a high-performance data visualization engine that utilizes both SVG for static elements and WebGL for hardware-accelerated rendering of large datasets and complex 3D plots. The library is distinguished by specialized toolkits for financial analysis, such as candlestick and OHLC charts, and geographic mapping tools for rendering choropleth and scatter maps with custom projections. It also supports complex scientific visualizations, including Sankey diagrams, pa

Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl

Seaborn is a Python library designed for statistical data visualization. It functions as a high-level interface built on the Matplotlib ecosystem, providing specialized routines to explore and communicate complex patterns within datasets. The framework enables users to generate informative graphics through automated statistical aggregation, multi-plot faceting, and integrated regression modeling. The library distinguishes itself through a declarative approach to data mapping, which translates raw inputs into visual properties like color, size, and position. It includes a robust statistical tr

This project is a scientific visualization guide and data visualization framework designed for creating high-quality 2D and 3D figures for academic journals and scientific publishing. It provides a structured approach to designing precise layouts, coordinate systems, and typography for complex scientific data. The project features a specialized print-ready PDF workflow and a CMYK print production workflow. These systems translate digital RGB colors into printer-specific CMYK profiles to ensure visual accuracy for physical hardcover and softcover printing. It also serves as a guide for SVG dat

jupyter-themes is a Jupyter Notebook theme manager and CSS interface customizer. It provides a command line tool to apply custom color schemes, fonts, and layout styles to notebook environments. The project includes a data visualization styling tool that synchronizes the aesthetic properties and color schemes of plotting libraries with the active interface theme. This ensures that data charts and figures remain visually consistent with the overall workspace theme.

SciencePlots is a Matplotlib style library and scientific plotting framework designed to automate the formatting of figures for academic journals and professional scientific publications. It provides a collection of visual presets and configuration rules for academic typography, layout, and resolution. The project features curated color-blind accessible palettes and figure formatters specifically designed to meet the strict submission standards of academic publishers. It includes specialized tools for professional figure styling and the rendering of non-Latin scripts for multilingual support.

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

react-vis is a declarative, component-based React data visualization library. It provides a framework of reusable building blocks for rendering interactive charts and graphs by mapping raw data to visual attributes such as position, color, and size. The system leverages D3 for its scaling and layout logic. The library is distinguished by its ability to handle complex data relationships, including hierarchical data via tree maps and circle packing, as well as multidimensional analysis using parallel axes and radar charts. It also supports network flow mapping to illustrate the volume and direc

fl_chart is a data visualization library and UI component framework for Flutter. It provides a system of reusable graphical widgets for creating interactive, customizable quantitative data visualizations. The framework supports a variety of chart types, including line, bar, pie, donut, scatter, radar, and candlestick views. It allows for the creation of complex visualizations such as layered data segments and financial charts. The library includes capabilities for interactivity and visual refinement, such as touch event handling, data tooltips, and state animations. It also provides tools fo

metrics-graphics is a data visualization library and declarative graphics framework designed to create principled data graphics and layouts. It functions as a statistical graphics engine that maps raw data to geometric shapes and structured objects to render complex, data-driven layouts. The toolkit specializes in rendering time-series data through line charts and scatterplots using a consistent layout system. It also provides capabilities for statistical distribution mapping, including the creation of rug plots to represent one-dimensional data density. The system covers a broad surface of

ggplot2 is a data visualization library for R based on a formal grammar of graphics. It provides a declarative plotting framework that allows users to create complex graphics by combining geometric objects, statistical summaries, and coordinate systems. The system is distinguished by a layered approach to composition, where visualizations are built incrementally by stacking independent geometric, statistical, and coordinate layers. It utilizes a hierarchical styling engine to manage non-data elements such as backgrounds, fonts, and margins, and includes a multi-panel faceting tool for splitti

ScottPlot is a cross-platform, high-performance charting library for .NET that renders interactive plots across desktop and web GUI frameworks including Windows Forms, WPF, MAUI, Avalonia, Blazor, and WinUI. It provides an optimized rendering engine capable of displaying millions of data points with interactive pan, zoom, and live data streaming, while also supporting image export to formats like PNG and SVG for file output, cloud applications, and notebooks. The library distinguishes itself through a comprehensive set of chart types including scatter, line, bar, pie, heatmap, financial, rada

Dieses Projekt ist ein Lehrplan für Machine Learning und eine Lernplattform, die über interaktive Jupyter Notebooks bereitgestellt wird. Es dient als umfassender Leitfaden zur Beherrschung des Python-Data-Science-Toolkits und bietet strukturierte Tutorials für numerisches Rechnen, Manipulation tabellarischer Daten und statistische Visualisierung. Der Lehrplan enthält spezifische Implementierungsleitfäden für Scikit-Learn und einen praktischen Kurs zu TensorFlow für den Aufbau, das Training und das Deployment neuronaler Netze und Computer-Vision-Modelle. Er deckt den End-to-End-Prozess des Aufbaus prädiktiver Modelle ab, von der anfänglichen Problemformulierung und Aufgabenkategorisierung bis hin zum Deployment der Modelle über interaktive Weboberflächen. Das Projekt deckt ein breites Funktionsspektrum ab, einschließlich numerischem Rechnen mit mehrdimensionalen Arrays, explorativer Datenanalyse und Datenvorverarbeitungsroutinen. Es bietet detaillierte Workflows für überwachtes und unüberwachtes Lernen, automatisierte Machine-Learning-Pipelines, Hyperparameter-Optimierung und Modellbewertung mittels Klassifizierungsmetriken und Kreuzvalidierung. Der Bildungsinhalt ist als eine Reihe von Notebooks strukturiert, die Python-Code mit narrativen Erklärungen verknüpfen, um Data-Science-Workflows zu dokumentieren.

Live-Charts ist eine .NET-Datenvisualisierungsbibliothek, die eine Sammlung interaktiver Diagramme, Karten und Messgeräte bereitstellt. Sie fungiert als Echtzeit-Charting-Engine und Multi-Format-Grafikbibliothek, die darauf ausgelegt ist, komplexe Datensätze innerhalb von .NET-Anwendungen zu rendern. Die Bibliothek bietet Tools zur Erstellung interaktiver Daten-Dashboards, die in der Lage sind, große Datensätze zu explorieren. Dies wird durch ein System zum Zoomen, Schwenken und Nutzen mehrerer Koordinatenachsen unterstützt, um durch Hunderttausende von Datenpunkten zu navigieren. Die Visualisierungs-Engine unterstützt eine Vielzahl von Formaten, einschließlich Balken-, Linien-, Heatmaps und geografischen Karten. Sie enthält Funktionen für die Echtzeit-Datenüberwachung und die Entwicklung von Desktop-Dashboards zur Verfolgung von Live-Metriken und Trends.

ScrollableGraphView is a Swift data visualization library and iOS plotting framework used to render discrete numerical datasets as interactive graphs. It provides a scrollable user interface component that visualizes data points using a coordinate system with configurable layouts and styling. The framework is characterized by its adaptive graph scaling, which automatically adjusts the vertical axis to fit the visible data points as the user scrolls. It supports real-time data rendering, allowing graph views to update instantly as underlying datasets change through animated transitions. The l

Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

Diese C++-Datenvisualisierungsbibliothek ist ein wissenschaftliches Plotting-Framework, das zum Erstellen von 2D- und 3D-Diagrammen, Netzwerk-Graphen und geografischen Karten verwendet wird. Sie arbeitet als Multi-Backend-Grafikbibliothek, die High-Level-Plotting-Logik von Low-Level-Rendering-Engines entkoppelt, um verschiedene Ausgabe-Backends zu unterstützen. Das Projekt zeichnet sich durch eine Dual-Interface-API aus, die sowohl ein globales funktionales Interface für schnelles Prototyping als auch ein objektorientiertes Interface für präzise Kontrolle bietet. Es verfügt über eine Komponenten-basierte Layout-Engine zur Verwaltung gekachelter Grids und Subplots, neben einem Layered-Plot-State, der es ermöglicht, mehrere Datenserien zu überlagern, ohne Achsen zu löschen. Die Bibliothek deckt ein breites Spektrum an Visualisierungsfunktionen ab, einschließlich mathematischem Funktionsplotten, Vektorfeldern und multidimensionaler Datenanalyse durch Heatmaps und parallele Koordinaten. Sie enthält spezialisierte Tools für die Visualisierung geografischer Daten, wie Geobubble- und Geodensity-Plots, sowie Tools zum Rendern gerichteter und ungerichteter Graphennetzwerke. Zu den allgemeinen Funktionen gehören Achsenverwaltung, ästhetisches Styling mit Colormaps und der Export hochwertiger Grafiken. Das Projekt nutzt CMake für Build-Automatisierung und Dependency-Retrieval, um die Installation über verschiedene Betriebssysteme hinweg zu erleichtern.