数据清洗练习

Lesson Overview

In "Python Quant Trading Lab" → "Python 金融数据处理", this lesson "数据清洗练习" builds knowledge you can apply immediately.

数据清洗不是让每行都通过，而是显式隔离无法解释的输入。重复时间、坏OHLC、缺失或非有限数值应分别计数，不能静默删除后宣称无异常。 下载脏CSV→保留原副本→跑清洗→核对有效/重复/隔离→查看异常原因→保存清洗哈希与特征表。 本课作业：复现240有效、1重复、3隔离；解释若同时间两行内容不同，为什么不能随便留最后一行。

Core Concepts

These points align with the video walkthrough—take notes as you watch.

• ▸数据清洗不是让每行都通过，而是显式隔离无法解释的输入。重复时间、坏OHLC、缺失或非有限数值应分别计数，不能静默删除后宣称无异常。
• ▸下载脏CSV→保留原副本→跑清洗→核对有效/重复/隔离→查看异常原因→保存清洗哈希与特征表。
• ▸本课作业：复现240有效、1重复、3隔离；解释若同时间两行内容不同，为什么不能随便留最后一行。

Practical Application

After studying, complete the practice tasks below to move from understanding to doing.

• ▸Follow along in Jupyter or VS Code
• ▸Log how parameter changes affect backtest results
• ▸Submit a reproducible analysis notebook

FAQ

• ▸Q: How does this lesson fit the course? A: Part of "Python 金融数据处理"—follow the curriculum order.
• ▸Q: What do I need? A: Demo account and notes; quant lessons need a Python environment.
• ▸Q: How do I know I've mastered it? A: Pass the quiz and explain three key points of 数据清洗练习 on your own.