Python数据分析 - PolarsBook中文版: https://www.pythondataanalysis.com/docs/polars_book_cn/ - Polars快速入门: https://www.pythondataanalysis.com/docs/polars_book_cn/quickstart/ - Polars表达式: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/ - Polars表达式: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/expressions/ - Polars上下文: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/contexts/ - Polars分组: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/groupby/ - Polars折叠: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/folds/ - Polars自定义函数: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/custom_functions/ - Polars实例: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/introduction_polars/ - Polars表达式方法: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/api/ - Polars视频介绍: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/video_intro/ - Polars与Numpy交互: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/numpy/ - Polars窗口函数: https://www.pythondataanalysis.com/docs/polars_book_cn/dsl/window_functions/ - Polars索引: https://www.pythondataanalysis.com/docs/polars_book_cn/indexing/ - Polars数据类型: https://www.pythondataanalysis.com/docs/polars_book_cn/datatypes/ - 来自Pandas: https://www.pythondataanalysis.com/docs/polars_book_cn/coming_from_pandas/ - 来自ApacheSpark: https://www.pythondataanalysis.com/docs/polars_book_cn/coming_from_spark/ - Polars性能: https://www.pythondataanalysis.com/docs/polars_book_cn/performance/ - 字符串: https://www.pythondataanalysis.com/docs/polars_book_cn/performance/strings/ - Polars优化: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/ - Polars惰性方法: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/ - 谓词下推: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/predicate-pushdown/ - 投影下推: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/projection-pushdown/ - 其它优化: https://www.pythondataanalysis.com/docs/polars_book_cn/optimizations/lazy/other-optimizations/ - Polars参考指南: https://www.pythondataanalysis.com/docs/polars_book_cn/references/ - Polars时间序列: https://www.pythondataanalysis.com/docs/polars_book_cn/timeseries/ - Polars时间序列实例: https://www.pythondataanalysis.com/docs/polars_book_cn/timeseries/time-series/ - Polars使用范围: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/ - IO: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/ - Polars操作CSV文件: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/csv/ - Polars操作Parquet文件: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/parquet/ - Polars处理多个文件: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/multiple_files/ - Polars读取数据库: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/read_db/ - Polars与AWS交互: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/aws/ - Polars与Google BigQuery交互: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/google-big-query/ - Polars与Postgres交互: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/io/postgres/ - 互通性: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/interop/ - Arrow: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/interop/arrow/ - Numpy: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/interop/numpy/ - 数据: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/data/ - 字符串: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/data/strings/ - 时间戳: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/data/timestamps/ - 数据帧: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/ - 选中行或列: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/row_col_selection/ - 常用操作: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/common-manipulations/ - 聚合: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/aggregate/ - 分组: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/groupby/ - 过滤: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/filter/ - 连接: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/join/ - 重塑: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/melt/ - 条件应用: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/conditionally-apply/ - 排序: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/sorting/ - 透视: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/df/pivot/ - 应用: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/apply/ - Polars自定义函数: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/apply/udfs/ - Polars窗口函数: https://www.pythondataanalysis.com/docs/polars_book_cn/howcani/apply/window-functions/ - Python数据分析 第二版: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/ - 第 1 章 准备工作: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-01/ - 第 2 章 Python 语法基础: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-02/ - 第 3 章 Python 的数据结构、函数和文件: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-03/ - 第 4 章 NumPy 基础：数组和向量计算: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-04/ - 第 5 章 Pandas 入门: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-05/ - 第 6 章 数据加载、存储与文件格式: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-06/ - 第 7 章 数据清洗和准备: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-07/ - 第 10 章 数据聚合与分组运算: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-10/ - 第 11 章 时间序列: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-11/ - 第 12 章 pandas 高级应用: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-12/ - 第 13 章 Python 建模库介绍: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-13/ - 第 14 章 数据分析案例: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-14/ - 附录 A NumPy 高级应用: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Appendix-A/ - 附录 B 更多关于 IPython 的内容: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Appendix-B/ - 第 8 章 数据规整：聚合、合并和重塑: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-08/ - 第 9 章 绘图和可视化: https://www.pythondataanalysis.com/docs/Python_Data_Analysis_2nd_Editon/Chapter-09/ - Polars用户指南: https://www.pythondataanalysis.com/docs/Polars_user_guide/ - Polars入门: https://www.pythondataanalysis.com/docs/Polars_user_guide/polars_getting_started/ - 安装Polars: https://www.pythondataanalysis.com/docs/Polars_user_guide/polars_installation/ - Polars核心概念: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/ - Polars数据类型和结构: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/data-types-and-structures/ - Polars表达式和上下文: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/expressions-and-contexts/ - Polars延迟API: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/lazy-api/ - Streaming: https://www.pythondataanalysis.com/docs/Polars_user_guide/concepts/_streaming/ - Polars表达式: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/ - Polars基本操作: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/basic-operations/ - Aggregation: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/aggregation/ - Casting: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/casting/ - Categorical Data and Enums: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/categorical-data-and-enums/ - Expression Expansion: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/expression-expansion/ - Folds: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/folds/ - Lists and Arrays: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/lists-and-arrays/ - Missing Data: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/missing-data/ - Numpy Functions: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/numpy-functions/ - Strings: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/strings/ - Structs: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/structs/ - User Defined Python Functions: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/user-defined-python-functions/ - Window Functions: https://www.pythondataanalysis.com/docs/Polars_user_guide/expressions/window-functions/ - Reference: https://www.pythondataanalysis.com/docs/Polars_user_guide/api/reference/ - Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/development/contributing/ - Versioning: https://www.pythondataanalysis.com/docs/Polars_user_guide/development/versioning/ - Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/polars-cloud/ - Ecosystem: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/ecosystem/ - Gpu Support: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/gpu-support/ - Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/io/ - Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/lazy/ - Pandas: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/migration/pandas/ - Spark: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/migration/spark/ - Arrow: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/arrow/ - Comparison: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/comparison/ - Multiprocessing: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/multiprocessing/ - Polars Llms: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/polars_llms/ - Styling: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/styling/ - Visualization: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/misc/visualization/ - Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/plugins/ - Create: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/create/ - Cte: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/cte/ - Intro: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/intro/ - Select: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/select/ - Show: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/sql/show/ - Index: https://www.pythondataanalysis.com/docs/Polars_user_guide/user-guide/transformations/ # Polars数据类型 `Polars`完全基于`Arrow`数据类型，并由`Arrow`内存阵列支持。这使得数据处理缓存效率高，支持进程间通信。大多数数据类型遵循确切的实现来自`Arrow`，除了`Utf8`（实际上是`LargeUtf8`）、`category`和`Object`（支持有限）。 这些数据类型是: - `Int8`: 8位有符号整数。 - `Int16`: 16位有符号整数。 - `Int32`: 32位有符号整数。 - `Int64`: 64位有符号整数。 - `UInt8`: 8位有符号整数。 - `UInt16`: 16位无符号整数。 - `UInt32`: 32位无符号整数。 - `UInt64`: 64位无符号整数。 - `Float32`: 32位浮点数。 - `Float64`: 64位浮点数。 - `Boolean`: 布尔型有效位压缩。 - `Utf8`: 字符串数据（内部实际上是`Arrow ` `LargeUtf8`）。 - `List`: 列表数组包含着包含列表值的子数组和偏移数组。（这实际上是内部的`Arrow` `LargeList`）。 - `Date`: 日期表示，内部表示为自UNIX纪元以来的天数，由32位有符号整数编码。 - `Datetime`: Datetime表示法，内部表示为自UNIX纪元以来的纳秒，由64位有符号整数编码。 - `Duration`: 时间型。在减去`Date/Datetime`时创建。 - `Time`: 时间表示法，从午夜开始在内部表示为纳秒。 - `Object`: 受支持的有限数据类型，可以是任何值。 要了解有关这些数据类型的内部表示形式的更多信息，请查看[`Arrow`柱状格式](https://arrow.apache.org/docs/format/Columnar.html)。