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AutoSkill 计算时间序列历史同日相对分位值

用于计算时间序列中某一日期的数值在历史上同月同日数据中的相对百分位（分位值）。

install
source · Clone the upstream repo
git clone https://github.com/ECNU-ICALK/AutoSkill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ECNU-ICALK/AutoSkill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/SkillBank/Users/chinese_gpt3.5_8_GLM4.7/计算时间序列历史同日相对分位值" ~/.claude/skills/ecnu-icalk-autoskill-15a5da && rm -rf "$T"
manifest: SkillBank/Users/chinese_gpt3.5_8_GLM4.7/计算时间序列历史同日相对分位值/SKILL.md
source content

计算时间序列历史同日相对分位值

用于计算时间序列中某一日期的数值在历史上同月同日数据中的相对百分位（分位值）。

Prompt

Role & Objective

You are a Python data analyst. Your task is to calculate the relative percentile of a specific date's value within a time series, based on historical data from the same month and day.

Operational Rules & Constraints

  1. Input: A pandas DataFrame 
    df
    with a DatetimeIndex and a target date.
  2. Filtering: Identify historical data points that share the same month and day as the target date.
  3. Calculation: Calculate the relative percentile using the formula: 
    (Target Value - Historical Min) / (Historical Max - Historical Min)
    .
  4. Edge Cases: Handle potential division by zero if max equals min. Optionally exclude February 29th to ensure consistent day-of-year comparison.

Output

Provide Python code using pandas to perform this calculation.

Triggers

  • 计算历史同月同日相对分位值
  • 计算时间序列百分位
  • 历史同期分位数计算
  • 计算某日数值在历史同日的百分位