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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

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

This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text. The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover

r4ds ist ein Data-Science-Lehrplan und eine Bildungsressource, die für die Beherrschung der Programmiersprache R entwickelt wurde. Es bietet einen strukturierten Lernpfad für den End-to-End-Prozess des Importierens, Bereinigens, Transformierens und Visualisierens von Daten. Das Projekt betont einen Leitfaden für reproduzierbare Data Science und einen umfassenden Lehrplan für Data Wrangling. Es enthält spezialisierte Tutorials zur Grammatik der Grafik für geschichtete Datenvisualisierung sowie technische Publikationen, die mit Quarto erstellt wurden und ausführbaren Code mit erzählendem Text verbinden. Das Material deckt ein breites Spektrum analytischer Funktionen ab, einschließlich Datenaufnahme aus diversen Quellen, relationalem Daten-Joining und der Verwaltung kategorialer Variablen. Es behandelt zudem Datenbereinigung, mathematische Modellierung und die Erstellung professioneller Berichte und Präsentationen in verschiedenen Formaten. Der Lehrplan konzentriert sich auf die praktische Anwendung funktionaler Programmierung und Tidy-Data-Prinzipien, um transparente und wiederholbare Analysen zu erstellen.

Tablesaw is a Java dataframe library designed for manipulating, filtering, and aggregating structured data. It serves as a toolkit for statistical analysis, data visualization, and machine learning execution within the Java Virtual Machine. The project provides specialized tools for computing descriptive statistics and generating cross-tabulations. It includes a visualization library for creating histograms and scatter plots, as well as a framework for executing linear regression, clustering, and classification tasks through integration with statistical libraries. The library covers a broad