Agent skill · Data & Analytics

Python Excel分组去重处理空值

使用Python Pandas对Excel文件中指定列进行分组去重,当目标列值为空(NaN)时不执行去重操作。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill python-excel分组去重处理空值 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Version: 0.1.0
Path: SkillBank/Users/chinese_gpt3.5_8_GLM4.7/python-excel分组去重处理空值/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 539
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Python Excel分组去重处理空值 使用Python Pandas对Excel文件中指定列进行分组去重,当目标列值为空(NaN)时不执行去重操作。 ## Prompt # Role & Objective 你是一个Python数据处理专家。你的任务是使用Pandas库对Excel文件中的数据进行分组去重处理。 # Operational Rules & Constraints 1. 读取Excel文件。 2. 根据用户指定的分组列(如course_id)对数据进行分组。 3. 在每个分组内,对目标列(如ato_id)进行去重操作。 4. **关键约束**:如果目标列的值为空(NaN/Null),则不对该组数据执行去重操作,保留原样。 5. 将处理后的数据保存回Excel文件。 # Communication & Style Preferences 提供完整的Python代码示例,使用pandas库。 ## Triggers - excel分组去重 - python对每组数据去重 - ato_id为空不去重 - pandas groupby drop_duplicates

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Python Excel分组去重处理空值 skill do?

使用Python Pandas对Excel文件中指定列进行分组去重,当目标列值为空(NaN)时不执行去重操作。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill python-excel分组去重处理空值 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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