Awesome GitHub RepositoriesPlot Styling Configurators

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

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

ScrollableGraphView 是一个 Swift 数据可视化库和 iOS 绘图框架，用于将离散数值数据集渲染为交互式图表。它提供了一个可滚动的用户界面组件，使用具有可配置布局和样式的坐标系来可视化数据点。 该框架的特点是其自适应图表缩放，当用户滚动时，它会自动调整垂直轴以适应可见数据点。它支持实时数据渲染，允许图表视图随着底层数据集通过动画过渡发生变化而即时更新。 该库涵盖了多种图表类型，包括折线图、柱状图和点图，并支持多数据集绘图以在单个图表上显示多个数据系列。其他功能包括 X 轴数据点标注、自定义图表样式，以及使用参考线标记来突出显示特定阈值或基准值。

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

这个 C++ 数据可视化库是一个科学绘图框架，用于创建 2D 和 3D 图表、网络图和地理地图。它作为一个多后端图形库运行，将高级绘图逻辑与低级渲染引擎解耦，以支持各种输出后端。 该项目以其双接口 API 脱颖而出，既提供用于快速原型的全局函数接口，也提供用于精确控制的面向对象接口。它具有一个用于管理平铺网格和子图的基于组件的布局引擎，以及一个允许在不清除坐标轴的情况下叠加多个数据系列的层级绘图状态。 该库涵盖了广泛的可视化功能，包括数学函数绘图、向量场，以及通过热力图和平行坐标进行的多维数据分析。它包括用于地理数据可视化的专用工具（如地理气泡图和地理密度图），以及用于渲染有向和无向图网络的工具。通用功能包括坐标轴管理、带有色图的美学样式，以及高质量图形的导出。 该项目利用 CMake 进行构建自动化和依赖检索，以促进在不同操作系统上的安装。

mplfinance 是一个基于 Matplotlib 构建的金融时间序列绘图和市场数据可视化框架。它旨在将市场数据帧渲染为专业图表，包括蜡烛图、OHLC 条形图、Renko 砖形图以及点数图（point-and-figure）。 该库的独特之处在于其专用的市场数据框架，该框架管理交易日历和非交易时段，通过在节假日期间折叠间隙来确保准确的时间间隔。它还提供了一个用于技术分析绘图的系统，能够在价格走势图上叠加移动平均线、成交量柱状图和其他技术指标。 该工具包涵盖了广泛的功能，包括组织具有共享轴的垂直堆叠子图以及应用一致的视觉主题。它支持市场标注（如趋势线）、缺失数据处理以及为实时数据源刷新图表的能力。可视化结果可导出为 PDF、SVG、PNG 和 JPG 等多种格式。

Makie.jl is a high-performance Julia data visualization library and hardware-accelerated plotting engine used to create interactive 2D and 3D visualizations. It functions as a reactive visualization framework where plots update automatically via observables and compute graphs, and as a vector graphics generator for high-resolution academic output. The system is distinguished by its backend-agnostic rendering pipeline, which supports OpenGL, WebGL, and ray-traced scenes. It employs a grammar-of-graphics approach to map variables to aesthetic attributes and utilizes a hierarchical scene graph t

Patchwork is a layout manager for combining multiple ggplot2 graphics into a single complex arrangement. It functions as a multi-plot composition tool and data visualization orchestrator, allowing independent graphics to be arranged into grids and nested layouts using additive and functional syntax. The system differentiates itself through a broadcast-based style application that propagates themes and scales across all subplots to maintain visual consistency. It also features guide-merging reconciliation to identify and collapse redundant legends into a single shared global guide. The framew