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

本项目是一个机器学习教育课程和学习平台，通过交互式 Jupyter Notebooks 提供。它作为掌握 Python 数据科学工具包的综合指南，为数值计算、表格数据操作和统计可视化提供结构化教程。 该课程包括 Scikit-Learn 的具体实现指南，以及关于构建、训练和部署神经网络及计算机视觉模型的 TensorFlow 实践课程。它涵盖了构建预测模型的端到端过程，从初始问题定义和任务分类，到通过交互式 Web 界面部署模型。 该项目涵盖了广泛的功能领域，包括多维数组的数值计算、探索性数据分析和数据预处理例程。它为监督和无监督学习、自动化机器学习流水线、超参数优化以及使用分类指标和交叉验证的模型评估提供了详细的工作流。 教育内容组织为一系列 Notebook，将 Python 代码与叙述性解释交织在一起，以记录数据科学工作流。

Live-Charts 是一个 .NET 数据可视化库，提供了一系列交互式图表、地图和仪表盘。它作为一个实时图表引擎和多格式图形库，旨在在 .NET 应用程序中渲染复杂数据集。 该库具有用于创建能够探索大型数据集的交互式数据仪表盘的工具。这由一个用于缩放、平移和利用多个坐标轴来导航数十万个数据点的系统所支持。 该可视化引擎支持多种格式，包括柱状图、折线图、热力图和地理地图。它包括用于实时数据监控和开发桌面仪表盘以跟踪实时指标和趋势的功能。

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。