python-pandas
Standardizes Pandas code by favoring vectorization over loops and enforcing project-wide naming and typing conventions.
Install
mkdir -p .claude/skills/python-pandas && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16970" && unzip -o skill.zip -d .claude/skills/python-pandas && rm skill.zipInstalls to .claude/skills/python-pandas
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Operate on entire Series and DataFrames using vectorized methods
- →Use `pd.NA`/`pd.NaT` for missing values instead of `np.nan`
- →Prefer nullable extension dtypes like `Int64`, `boolean`, `string`
- →Type-hint DataFrame parameters and returns with `DataFrame[Model]` using Pandera
- →Suffix DataFrame variables with `_df` for type visibility
- →Name Series variables for their contents
How it works
This skill enforces a set of coding standards for Pandas, promoting vectorized operations, Pandas-native types for missing values, and Pandera schemas for type-hinting. It guides the use of Series methods over NumPy functions and specific naming conventions.
Inputs & outputs
When to use python-pandas
- →Transforming DataFrames
- →Handling missing data values
- →Type-hinting tabular data
- →Optimizing data pipelines
About python-pandas
Enforces Pandas best practices by replacing inefficient loops with vectorized operations. It mandates standard naming for DataFrames and Series, encourages nullable types, and prevents mixing NumPy with Pandas.
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.
When not to use it
- →When the task requires iterating over rows with `.iterrows()`
- →When the task involves mixing NumPy arrays mid-pipeline
- →When the task does not involve Pandas DataFrames or Series
Limitations
- →Does not support using `.iterrows()` for iteration
- →Does not support mixing NumPy arrays with Pandas types mid-pipeline
- →Does not support using `np.nan` for missing values
How it compares
This skill provides explicit guidelines for writing idiomatic and efficient Pandas code, contrasting with a generic approach that might use inefficient loops or non-native types.
Compared to similar skills
python-pandas side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| python-pandas (this skill) | 0 | 3mo | No flags | Intermediate |
| jupyter-notebook | 30 | 7mo | Review | Intermediate |
| sexp | 3 | 7mo | No flags | Advanced |
| r-code | 0 | 6mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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