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/

# Generating Polars code with LLMs

Large Language Models (LLMs) can sometimes return Pandas code or invalid Polars code in their
output. This guide presents approaches that help LLMs generate valid Polars code more consistently.

These approaches have been developed by the Polars community through testing model responses to
various inputs. If you find additional effective approaches for generating Polars code from LLMs,
please raise a [pull request](https://github.com/pola-rs/polars/pulls).

## System prompt

Many LLMs allow you to provide a system prompt that is included with every individual prompt you
send to the model. In the system prompt, you can specify your preferred defaults, such as "Use
Polars as the default dataframe library". Including such a system prompt typically leads to models
consistently generating Polars code rather than Pandas code.

You can set this system prompt in the settings menu of both web-based LLMs like ChatGPT and
IDE-based LLMs like Cursor. Refer to each application's documentation for specific instructions.

## Enable web search

Some LLMs can search the web to access information beyond their pre-training data. Enabling web
search allows an LLM to reference up-to-date Polars documentation for the current API.

Some IDE-based LLMs can index the Polars API documentation and reference this when generating code.
For example, in Cursor you can add Polars as a custom docs source and instruct the agent to
reference the Polars documentation in a prompt.

However, web search does not yet guarantee that valid code will be produced. If a model is confident
in a result based on its pre-training data, it may not incorporate web search results in its output.

The Polars API pages also have AI-enabled search to help you find the information you need more
easily.

## Provide examples

You can guide LLMs to use correct syntax by including relevant examples in your prompt.

For instance, this basic query:

```python
df = pl.DataFrame({
    "id": ["a", "b", "a", "b", "c"],
    "score": [1, 2, 1, 3, 3],
    "year": [2020, 2020, 2021, 2021, 2021],
})
# Compute average of score by id
```

Often results in outdated `groupby` syntax instead of the correct `group_by`.

However, including a simple example from the Polars `group_by` documentation (preferably with web
search enabled) like this:

```python
df = pl.DataFrame({
    "id": ["a", "b", "a", "b", "c"],
    "score": [1, 2, 1, 3, 3],
    "year": [2020, 2020, 2021, 2021, 2021],
})
# Compute average of score by id
# Examples of Polars code:

# df.group_by("a").agg(pl.col("b").mean())
```

Produces valid outputs more often. This approach has been validated across several leading models.

The combination of web search and examples is more effective than either independently. Model
outputs indicate that when an example contradicts the model's pre-trained expectations, it seems
more likely to trigger a web search for verification.

Additionally, explicit instructions like "use `group_by` instead of `groupby`" can be effective in
guiding the model to use correct syntax.

Common examples such as `df.group_by("a").agg(pl.col("b").mean())` can also be added the system
prompt for more consistency.