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DataFrame行内非空非零元素分位数计算 — measured AI agent skill | DecimalAI

ecnu-icalk/DataFrame行内非空非零元素分位数计算

NewSkillScoreMaintained

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。

Databy ecnu-icalk

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Data Analyst pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/ecnu-icalk-dataframe/SKILL.md

decimalai skills pull ecnu-icalk-dataframe --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/ecnu-icalk-dataframe/SKILL.md https://app.decimal.ai/s/ecnu-icalk-dataframe/SKILL.md

Raw file: /s/ecnu-icalk-dataframe/SKILL.md ↗ · machine-readable summary at /s/ecnu-icalk-dataframe.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 2h ago

Yes — +5% lift-8% tokens0% turns100% of whole cases pass with skill (22/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	-8%	0%
case-15	✓→✓	＝ Same ✓	0%	0%
case-16	✓→✓	＝ Same ✓	-22%	0%
case-02	✓→✓	＝ Same ✓	-4%	0%
case-03	✓→✓	＝ Same ✓	1%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

ecnu-icalk / autoskill

SkillBank/ConvSkill/chinese_gpt3.5_8/dataframe%E8%A1%8C%E5%86%85%E9%9D%9E%E7%A9%BA%E9%9D%9E%E9%9B%B6%E5%85%83%E7%B4%A0%E5%88%86%E4%BD%8D%E6%95%B0%E8%AE%A1%E7%AE%97/SKILL.md

View on GitHub ↗

Imported 8/3/2026Version v1

Skill contents

SKILL.md

DataFrame行内非空非零元素分位数计算

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。

Prompt

Role & Objective

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。

Operational Rules & Constraints

计算范围：必须按行（axis=1）进行操作。
过滤条件：在计算排名前，必须排除空值和零值。即只对满足 notna() 且 != 0 的元素进行计算。
计算方法：使用 rank(pct=True) 方法计算百分比排名（分位数）。
输出结构：返回结果应保持原DataFrame的形状，被过滤掉的元素（空值或零值）在结果中应为NaN。

Anti-Patterns

不要计算全局分位数，必须是基于行的。
不要将零值或空值纳入排名计算。
不要简单地删除行或列，而是进行映射计算。

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

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