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/ # Ecosystem ## Introduction On this page you can find a non-exhaustive list of libraries and tools that support Polars. As the data ecosystem is evolving fast, more libraries will likely support Polars in the future. One of the main drivers is that Polars makes adheres its memory layout to the `Apache Arrow` spec. ### Table of contents: - [Apache Arrow](#apache-arrow) - [Data visualisation](#data-visualisation) - [IO](#io) - [Machine learning](#machine-learning) - [Other](#other) --- ### Apache Arrow [Apache Arrow](https://arrow.apache.org/) enables zero-copy reads of data within the same process, meaning that data can be directly accessed in its in-memory format without the need for copying or serialisation. This enhances performance when integrating with different tools using Apache Arrow. Polars is compatible with a wide range of libraries that also make use of Apache Arrow, like Pandas and DuckDB. ### Data visualisation See the [dedicated visualization section](misc/visualization.md). ### IO #### Delta Lake The [Delta Lake](https://github.com/delta-io/delta-rs) project aims to unlock the power of the Deltalake for as many users and projects as possible by providing native low-level APIs aimed at developers and integrators, as well as a high-level operations API that lets you query, inspect, and operate your Delta Lake with ease. Delta Lake builds on the native Polars Parquet reader allowing you to write standard Polars queries against a DeltaTable. Read how to use Delta Lake with Polars [at Delta Lake](https://delta-io.github.io/delta-rs/integrations/delta-lake-polars/#reading-a-delta-lake-table-with-polars). ### Machine Learning #### Scikit Learn The [Scikit Learn](https://scikit-learn.org/stable/) machine learning package accepts a Polars `DataFrame` as input/output to all transformers and as input to models. #### XGBoost & LightGBM XGBoost and LightGBM are gradient boosting packages for doing regression or classification on tabular data. [XGBoost accepts Polars `DataFrame` and `LazyFrame` as input](https://xgboost.readthedocs.io/en/latest/python/python_intro.html) while LightGBM accepts Polars `DataFrame` as input. #### Time series forecasting The [Nixtla time series forecasting packages](https://nixtlaverse.nixtla.io/statsforecast/docs/getting-started/getting_started_complete_polars.html) accept a Polars `DataFrame` as input. #### Hugging Face Hugging Face is a platform for working with machine learning datasets and models. [Polars can be used to work with datasets downloaded from Hugging Face](io/hugging-face.md). #### Deep learning frameworks A `DataFrame` can be transformed [into a PyTorch format using `to_torch`](https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.to_torch.html) or [into a JAX format using `to_jax`](https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.to_jax.html). ### Other #### DuckDB [DuckDB](https://duckdb.org) is a high-performance analytical database system. It is designed to be fast, reliable, portable, and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs), and more. Read about integration with Polars [on the DuckDB website](https://duckdb.org/docs/guides/python/polars). #### Great Tables With [Great Tables](https://posit-dev.github.io/great-tables/articles/intro.html) anyone can make wonderful-looking tables in Python. Here is a [blog post](https://posit-dev.github.io/great-tables/blog/polars-styling/) on how to use Great Tables with Polars. #### LanceDB [LanceDB](https://lancedb.com/) is a developer-friendly, serverless vector database for AI applications. They have added a direct integration with Polars. LanceDB can ingest Polars dataframes, return results as polars dataframes, and export the entire table as a polars lazyframe. You can find a quick tutorial in their blog [LanceDB + Polars](https://blog.lancedb.com/lancedb-polars-2d5eb32a8aa3) #### Mage [Mage](https://www.mage.ai) is an open-source data pipeline tool for transforming and integrating data. Learn about integration between Polars and Mage at [docs.mage.ai](https://docs.mage.ai/integrations/polars). #### marimo [marimo](https://marimo.io) is a reactive notebook for Python and SQL that models notebooks as dataflow graphs. It offers built-in support for Polars, allowing seamless integration of Polars dataframes in an interactive, reactive environment - such as displaying rich Polars tables, no-code transformations of Polars dataframes, or selecting points on a Polars-backed reactive chart.