菜单
Write Pandas code to this project's standards — vectorised operations, Pandas-native types, and Pandera schemas. Use when importing or using pandas — transforming DataFrames or Series, handling missing values, or type-hinting tabular data.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Write agent output for the accountable human reviewer — route narrative by audience and lifespan, lead with a budgeted summary, keep detail collapsed, and plan before building. Use when writing PR or issue descriptions, commit messages, or code comments, when addressing or resolving PR review comments, when deciding whether to raise an issue or where work-narrative belongs, or when scoping a non-trivial change before generating code.
Write asyncio Python to this project's standards — concurrent coroutines, async-all-the-way, and safe handling of errors from concurrent tasks. Use when writing async/await code, gathering coroutines, designing async FastAPI endpoints or clients, or handling failures from concurrent work.
Write Python source to this project's standards. Use when authoring or refactoring Python code and deciding on style, type hints, docstrings, comments, imports, or language idioms.
Work with dates, times, and timezones to this project's standards. Use when creating, parsing, serialising, comparing, or storing datetimes, or handling timezones.
Handle errors in Python to this project's standards — custom domain exceptions, narrow catches, and failing fast. Use when raising or catching exceptions, writing try/except, designing an exceptions module, or deciding whether to swallow or propagate an error.
Reach for this project's preferred, well-supported libraries instead of hand-rolling. Use when choosing a library for HTTP, retries, JSON, or parsing, or whenever you are about to write something the standard library or a maintained package already does.
| name | python-pandas |
| description | Write Pandas code to this project's standards — vectorised operations, Pandas-native types, and Pandera schemas. Use when importing or using pandas — transforming DataFrames or Series, handling missing values, or type-hinting tabular data. |
Standards for working with Pandas DataFrames and Series. Extends the "prefer library
idioms" rule in python-code-style with Pandas specifics.
.apply, .iterrows, or a
Python for over rows. Reach for Series methods — .where/.mask/.map/.clip/
.str.*, .between, .isin — and df.eval/df.query for arithmetic and filtering.Series.map(mapping) to .apply(lambda x: mapping[x]), and use &/| only for
row-wise boolean masks — keep and/or for scalar conditionals..itertuples() (named, typed, fast), never
.iterrows()._df so the type is visible at the call site; name Series
for their contents (e.g. unit_price, not s or data).pd.NA/pd.NaT for missing values, not np.nan, and prefer the nullable
extension dtypes (Int64, boolean, string) so missingness is first-class — NumPy
float columns silently coerce pd.NA to NaN.np.* functions on a Series (including via .apply), and
never use .values (the PD011 lint flags it) — reach for .to_numpy() only at a
boundary needing a raw array.DataFrame[Model].
The payoff is readability — the schema becomes explicit at every reference — so use it
ubiquitously, not sparingly.Category dtype built from an Enum's values (iterate
the Enum to build the categories), and annotate timestamp columns as pd.Timestamp.