6 个仓库
Instructional materials for manipulating and analyzing structured data using scientific Python libraries.
Distinct from Python Data Deserialization: The candidates focus on specific technical tasks (deserialization, pipeline frameworks) rather than educational resources.
Explore 6 awesome GitHub repositories matching education & learning resources · Python Data Analysis Tutorials. Refine with filters or upvote what's useful.
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
Provides educational guides and code examples for performing data manipulation and statistical analysis using Python.
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
Provides code snippets for cleaning, manipulating, and analyzing tabular data using scientific libraries.
This project is a collection of educational notes and tutorials focused on Python programming, scientific computing, and data analysis. It serves as a reference for learning language basics, advanced techniques, and object-oriented design. The materials include implementation guides for building linear, logistic, and convolutional neural networks using symbolic graph frameworks. It also provides instruction on manipulating and visualizing structured data frames and performing complex mathematical operations through numerical libraries. The repository includes a system for converting interact
Offers instructional materials for manipulating and analyzing structured data using scientific Python libraries.
本项目是一个全面的 Python 编程教育材料合集,包括教程、练习与精选代码示例。它作为一个学习课程与软件工程工具包,利用 Jupyter Notebooks 将可执行代码与描述性教育文本相结合。 该仓库提供了构建大语言模型应用的实践指南,例如检索增强生成(RAG)系统、有状态 AI 代理与机器学习工作流。它通过提供结构化的代理编码工作流脱颖而出,涵盖了上下文窗口蒸馏、与提供商无关的模型路由以及模式强制的结构化输出。 这些材料涵盖了广泛的软件工程能力,包括使用分布式任务队列的异步编程、使用 REST API 的 Web 应用开发以及数据分析工作流。它还包括用于掌握面向对象设计、实现 CI/CD 流水线以及应用专业 Linting 与格式化标准的资源。
Provides instructional materials for manipulating and analyzing structured data using scientific Python libraries.
This project is a collection of educational resources and study materials focused on scientific computing and data analysis using Python. It consists of translated notes and Jupyter notebooks designed to guide learners through the Python data ecosystem. The content covers specialized workflows including numerical computation, data cleaning, and time series analysis. These materials provide a reference for performing complex data manipulations and processing sequential data to identify patterns. The resource is organized as a series of static files and markdown documents using a flat-file dir
Provides instructional materials for manipulating and analyzing structured data using scientific Python libraries.
This repository serves as an educational resource and structured curriculum for performing statistical analysis using Python. It provides a comprehensive guide to the scientific computing workflow, focusing on the practical application of data cleaning, numerical modeling, and distribution visualization. The tutorial covers the end-to-end process of transforming raw tabular data into actionable insights. It demonstrates how to manipulate structured datasets through merging and aggregation, perform descriptive and inferential statistical calculations, and fit regression models to evaluate rela
Provides a comprehensive guide for performing data cleaning, numerical modeling, and distribution visualization using standard scientific computing libraries.