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‎Lecture/01_homework.md‎

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## 作业展示
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- [2026 小组作业展示](../homework/HW-pre-2026.md)
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- [2026 小组作业展示](https://github.com/lianxhcn/dsfin/blob/main/homework/HW-pre-2026.md)

‎Lecture/data_clean/lecture_data_clean.ipynb‎

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"即使一份数据没有任何格式错误，它仍然可能因为「哪些观测进入了样本、哪些没有」这一机制，导致估计结果系统性偏误。这就是**样本选择偏差**（sample selection bias）。\n",
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"\n",
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"#### 一个直观的例子\n",
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"### 一个直观的例子\n",
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"\n",
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"我们的研究问题是：**上市公司的贷款利率（Rate）受哪些因素影响？**\n",
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"> **已知组的拟合线高估了规模对利率的负向效应。** 真实斜率（包含缺失组后）应更平缓。\n",
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"![](fig/fig_sample_selection.png){width=\"90%\"}\n",
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"![](fig/fig_data_clean_simulation_sample_selection.png){width=\"100%\"}\n",
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"\n",
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"图（a）同样值得关注：缺失组的杠杆率（Leverage）均值略高于已知组（0.71 vs 0.66），方向符合预期，但因样本量极小（n = 4），未达统计显著。在真实数据中，这类方向性证据仍需认真对待。\n",
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‎Lecture/data_get_data/data/GMD.csv‎

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‎Lecture/data_get_data/data_02_get_data_GMD.ipynb‎

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‎Lecture/data_get_data/data_03_TS_SZ_index.ipynb‎

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‎Lecture/data_get_data/lecture_get_data.ipynb‎

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‎Lecture/data_store_structure/lecture_data_management_organization.ipynb‎

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"数据管理中最核心的三个概念是：**数据表**、**主键** 和 **粒度**。\n",
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"### 2.1 数据表\n",
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"### 数据表",
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"一张数据表可以理解为：对某一类观测单位在某一层级上的系统记录。例如：\n",
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"- 日度收益率表：每行是一个公司-日；\n",
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"- 公告元数据表：每行是一篇公告。\n",
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"### 2.2 主键\n",
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"### 主键",
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"主键可以理解为：**唯一标识一行数据的变量或变量组合**。例如：\n",
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"K_i \\neq K_j \\quad \\text{for all } i \\neq j\n",
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"### 2.3 粒度\n",
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"### 粒度",
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"粒度可以理解为：**数据记录的细致程度**。在金融数据中，最常见的粒度包括：\n",
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"### 2.4 一个典型错误：忽略粒度直接合并\n",
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"### 一个典型错误：忽略粒度直接合并",
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"### 3.1 文件命名规则\n",
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"### 文件命名规则",
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"这样的命名方式比 `new_final_use_this.csv` 稳妥得多。文件名本身就能提供足够的上下文信息，便于快速判断其内容和用途。\n",
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"### 数据字典与 README",
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"### CSV / Excel",
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"### Parquet",
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"`Parquet` 是适合分析场景的列式存储格式。可以把它理解为：比 `CSV` 更现代、更高效的一种表格文件形式。它通常具有以下优点：\n",
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"### 4.3 SQLite\n",
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"### SQLite",
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"`SQLite` 是一种轻量级本地关系数据库。它与 `CSV` 的最大区别在于：它不是单纯的数据文件，而是一个可以使用 SQL 进行查询和连接的小型数据库系统。它尤其适合：\n",
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"### 4.4 DuckDB\n",
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"### DuckDB",
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"如果说 `SQLite` 更偏向轻量级关系数据库，那么 `DuckDB` 更适合分析型任务。它很适合处理多个本地大文件、多次聚合和连接，以及分析型 SQL 查询。\n",
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"### 6.1 继续使用 `pandas` 即可的情况\n",
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"### 继续使用 `pandas` 即可的情况",
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"### 可以考虑 SQLite 或 DuckDB 的情况",
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‎_quarto.yml‎

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‎docs/Lecture/00-setup/01_00_coding_with_AI.html‎

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