pandas
Pandas data manipulation with DataFrames. Use for data analysis.
Install
npx skillscat add g1joshi/agent-skills/pandas Install via the SkillsCat registry.
Pandas provides Python libraries for manipulating DataFrames and Series to load, clean, and analyze tabular data. It solves the difficulty of efficiently handling structured datasets and performing vectorized operations without manual loops. Use it when working with medium-sized data in memory that requires cleaning, transformation, or time‑series analysis.
Pandas
Pandas is the Excel of Python. v3.0 (2025/2026) enforces Copy-on-Write (CoW), finally fixing the SettingWithCopyWarning confusion.
When to Use
- Data Cleaning: Loading CSV/Excel/SQL and cleaning it.
- Time Series: Unmatched datetime indexing capabilities.
- Small/Medium Data: Features that fit in RAM.
Core Concepts
DataFrame / Series
2D tables and 1D arrays.
Copy-on-Write (CoW)
Views are always views, copies are always copies. Modifying a view triggers a copy only if necessary.
PyArrow Backend
Using Arrow memory format for speed and string handling (dtype="string[pyarrow]").
Best Practices (2025)
Do:
- Use PyArrow Strings:
pd.options.future.infer_string = True(Default in 3.0). - Use
.query(): For cleaner filtering syntax. - Migrate to CoW: Ensure your code doesn't rely on side-effects of views.
Don't:
- Don't iterate rows: Use vectorization (
df['a'] + df['b']).
References
Categories
Install
npx skillscat add g1joshi/agent-skills/pandas Install via the SkillsCat registry.