4 个仓库
General operations for altering the layout of data, including pivoting and unpivoting.
Distinct from Long-to-Wide Reshaping: Combines both long-to-wide and wide-to-long reshaping into a single conceptual capability
Explore 4 awesome GitHub repositories matching data & databases · Data Reshaping Operations. Refine with filters or upvote what's useful.
本项目是一个全面的 pandas 数据分析教程和指南,旨在帮助学习数据处理与分析。它涵盖了表格数据处理、时间序列分析,并提供了清洗、合并及转换数据集的结构化方法。 该仓库还充当数据特征工程课程,提供关于构建和选择数据集特征以提升机器学习模型性能的教程。此外,它还包含用于执行逐元素数学计算和矩阵操作的向量化数据处理指南。 内容涵盖了广泛的功能,包括数据清洗工作流、数据集成任务和表格数据分析。它还提供了处理文本信息、处理分类数据以及优化大规模数据集执行速度的指导。 项目以一系列 Jupyter Notebook 的形式呈现,包含实践练习和针对性的练习题。
Teaches how to reshape data between long and wide formats for improved reporting.
该项目是一个针对 R 的高性能表格数据处理框架,旨在以内存效率和速度处理海量数据集。它提供了一种增强的数据结构,利用引用语义和就地修改来执行复杂的转换,而无需不必要的对象复制开销。 该库凭借其底层架构优化脱颖而出,包括多线程并行处理、基数排序和内存映射文件解析。通过将关键的数据操作和聚合例程卸载到编译后的 C 代码,它实现了对原本计算昂贵的任务的快速执行。其核心引擎支持高级关系操作,如非等值连接、滚动连接和重叠区间连接,以及用于加速重复数据访问的自动二级索引。 除了主要的处理功能外,该项目还提供了一套全面的数据生命周期管理工具。这包括具有自动类型检测的高速摄取和序列化工具,以及对时间序列分析和多维聚合的专门支持。该框架旨在实现可扩展性,允许用户在包含数十亿行的数据集上执行复杂的分组、过滤和重塑操作,同时保持系统稳定性和性能。
Converts data between wide and long formats using melting and casting with pattern-based column selection.
MoreLINQ is a functional programming toolkit and extension library for .NET that augments LINQ to Objects with advanced operators for sequence manipulation and analysis. It provides a set of tools for declarative data transformation, leveraging lazy evaluation and composition to handle complex object sequences. The library is distinguished by its specialized capabilities for combinatorial generation, including the production of permutations, subsets, and Cartesian products. It also provides advanced sequence joining options, such as full, left, and right outer joins, and supports complex data
Provides operations for restructuring sequences via batching, windowing, and flattening.
This library is a data processing framework for the JVM that provides a type-safe environment for manipulating structured tabular data. It functions as a comprehensive toolset for performing complex data transformations, aggregations, and statistical analysis, while leveraging compile-time schema validation to ensure structural integrity across data pipelines. The project distinguishes itself through its deep integration with interactive notebook environments and its use of compile-time code generation. By automatically deriving and enforcing schemas from raw inputs, it generates type-safe ac
Reshapes grouped data into matrix-like structures by rotating column values into new headers.