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/

# Python数据分析

Python 是进行数据分析的强大工具，拥有丰富的库和工具，以下为你详细介绍 Python 数据分析的相关内容，包含基本流程、常用库以及一个简单示例。

### 基本流程
1. **数据收集**：从各种来源获取数据，如文件（CSV、Excel 等）、数据库、API 接口等。
2. **数据清洗**：处理缺失值、重复值、异常值等，确保数据的质量和一致性。
3. **数据探索**：使用统计方法和可视化技术对数据进行初步探索，了解数据的分布、特征和关系。
4. **数据分析**：运用各种分析方法，如描述性统计分析、相关性分析、机器学习算法等，从数据中提取有价值的信息。
5. **数据可视化**：将分析结果以直观的图表形式展示出来，如柱状图、折线图、散点图等，便于理解和沟通。
6. **结果呈现**：将数据分析的结果整理成报告或文档，向相关人员进行汇报。

### 常用库
- **[NumPy](/docs/Python_Data_Analysis_2nd_Editon/Chapter-04/)**：提供了高效的多维数组对象和各种数学函数，是许多数据分析库的基础。
- **[Pandas](/docs/Python_Data_Analysis_2nd_Editon/Chapter-05/)**：用于数据处理和分析，提供了 DataFrame 和 Series 等数据结构，方便进行数据的读取、清洗、转换和分析。
- **[Polars](/docs/Polars_user_guide/polars_getting_started/)**：速度极快的DataFrame库，用于操作结构化数据。核心基于Rust开发，可用于Python， R和NodeJS。
- **Matplotlib**：是 Python 中最常用的绘图库，可用于创建各种类型的静态图表。
- **Seaborn**：基于 Matplotlib 构建的高级可视化库，提供了更美观、更简洁的绘图接口。
- **Scikit-learn**：用于机器学习和数据挖掘，提供了各种机器学习算法和工具，如分类、回归、聚类等。