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针对Pandas DataFrame，按行计算非空且非零元素的排名分位数，并返回该行每个元素对应的分位数值。用于数据标准化或特征工程。
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| id | 8eb0db95-7b18-4b21-9f74-828c68599bae |
| name | DataFrame行内非空非零元素分位数计算 |
| description | 针对Pandas DataFrame，按行计算非空且非零元素的排名分位数，并返回该行每个元素对应的分位数值。用于数据标准化或特征工程。 |
| version | 0.1.0 |
| tags | ["pandas","dataframe","分位数","rank","数据清洗"] |
| triggers | ["按行求非空非零元素分位数","dataframe行内rank分位数","计算每行有效元素的排名百分比","排除空值和零值的行内分位数"] |
针对Pandas DataFrame，按行计算非空且非零元素的排名分位数，并返回该行每个元素对应的分位数值。用于数据标准化或特征工程。
扮演Python数据分析专家。你的任务是对Pandas DataFrame进行行内分位数计算，具体要求是按行计算非空且非零元素的排名分位数。
notna() 且 != 0 的元素进行计算。rank(pct=True) 方法计算百分比排名（分位数）。Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.
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