Awesome GitHub RepositoriesDataframe Visualizers

Pygwalker is a library that transforms tabular data into interactive, drag-and-drop interfaces for exploratory analysis and visualization. It functions as a grammar-based framework that translates user interactions into declarative chart definitions, allowing for the creation of dynamic data exploration environments directly within notebooks or embedded web applications. The system distinguishes itself by offloading heavy analytical computations to backend kernels, which maintains responsiveness when visualizing large datasets. It supports the serialization of visual states into portable conf

DearPyGui is a GPU-accelerated, immediate-mode graphical user interface framework for Python. It provides a high-performance toolkit for building interactive desktop applications by leveraging native hardware-accelerated rendering backends across multiple operating systems. By utilizing an immediate-mode execution model, the library offers direct control over the rendering loop and element state, enabling the creation of responsive, dynamic interfaces. The framework distinguishes itself through its ability to handle complex, high-frequency visual updates, making it suitable for real-time data

This library provides a diagnostic toolkit for automated data profiling and exploratory analysis. It generates comprehensive statistical summaries and visual reports for tabular datasets, enabling users to identify distribution patterns, missing values, and quality anomalies through a unified interface. The project distinguishes itself by offering differential analysis, which allows for the comparison of two dataset versions to track structural and statistical changes over time. It supports large-scale data processing through lazy evaluation and provides interactive widgets that embed directl

This project is an exploratory data analysis framework and profiling tool designed to generate comprehensive statistical reports from Pandas and Spark DataFrames. It functions as a data quality profiler that identifies missing values, duplicates, and high correlations within tabular datasets. The tool distinguishes itself through specialized capabilities for time-series analysis, extracting temporal statistics, seasonality, and auto-correlation plots. It also includes a dataset comparison utility to identify structural or content changes between different versions of a dataset. The analysis

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

Ydata-profiling is an automated exploratory data analysis framework designed to generate comprehensive statistical reports and visual summaries from dataframes. It functions as a diagnostic tool for assessing data quality, identifying missing values, duplicates, and outliers, while providing a scalable engine for profiling massive datasets across distributed enterprise environments. The project distinguishes itself through its ability to handle large-scale data through distributed task orchestration and lazy stream processing, which minimizes memory overhead during complex computations. It in

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。

Lux 是一款自动化探索性数据分析工具，旨在为 pandas 数据帧生成智能视觉表示。它通过根据数据集的统计属性推荐最佳图表类型和轴映射来识别模式和趋势。 该工具作为一个交互式数据分析层，允许用户使用过滤器和通配符浏览和查询图表集合。它还充当可视化代码生成器，将自动生成的图表转换为程序代码或 HTML，以便在外部库中进行手动优化。 该系统涵盖了广泛的探索性分析功能，包括自动图表编码、通过步骤推荐进行引导式发现，以及将视觉配置导出为声明式规范的能力。 该项目直接集成到 pandas 中，通过交互式可视化组件覆盖默认的数据帧打印方式。

dtale 是一个基于 Web 的 pandas 数据框交互式网格与可视化工具，设计为探索性数据分析工具。它提供了一个基于浏览器的界面用于分析表格数据结构，允许用户在无需编写手动代码的情况下计算统计数据、检测异常值并计算相关性。 该项目作为嵌入式数据查看器运行，可通过 iframe 或自定义路由集成到 Web 应用中，并对 Django、Flask 与 Streamlit 提供特定支持。它通过交互式数据网格与能够生成直方图、箱线图与 3D 散点图的数据可视化库的组合，实现了对数据集的探索。 该平台涵盖了广泛的数据管理与分析能力，包括表格数据清理、重塑与交互式过滤。它包括用于缺失数据分析、相关性计算与预测能力评分的观测工具。对于会话管理，它支持多实例追踪与跨并发工作进程的状态持久化。 该界面受用户名与密码认证保护，并支持从分隔文件、电子表格与 ArcticDB 数据存储中进行数据摄入。

Visual Insights is an automated exploratory data analysis platform and causal inference tool designed to discover patterns and cause-and-effect relationships within datasets. It functions as an interactive data visualization library using a grammar-of-graphics approach to generate multi-dimensional charts and dashboards. The project distinguishes itself through a natural language interface that translates plain-text questions into data answers and visualizations via a language model. It provides a specialized framework for causal discovery and inference, allowing users to identify variable li

missingno 是一个用于缺失数据模式可视化和分析的 Python 库。它提供了一套工具来分析数据集的完整性、映射数据缺口并量化变量中空值的数量。 该库通过空值相关性分析器和分层数据聚类工具脱颖而出。这些组件允许通过测量一个变量的缺失如何与另一个变量的缺失相关联，来检测系统性依赖和趋势。 该工具集涵盖了更广泛的数据质量审计和探索性分析功能。它包括使用线性和对数刻度进行列空值汇总的功能，以及用于识别记录中系统性缺口的基于矩阵的映射。

XlsxWriter is a library for generating spreadsheets in the XLSX format, functioning as an Excel workbook writer and file generator. It provides the capability to write data, apply cell formatting, and build complex layouts across multiple worksheets. The project distinguishes itself with a memory-optimized writing mode that flushes large datasets to disk row-by-row, enabling the creation of files exceeding 4 GB while minimizing RAM consumption. It also includes a specialized mechanism for embedding binary project files and digital signatures to enable VBA macros and signed scripts within work