Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling

Pandas Pro Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production grade performance patterns. Core Workflow 1. Assess data structure — Examine dtypes, memory usage, missing values, data quality: 2. Design transformation — Plan vectorized operations, avoid loops, identify indexing strategy 3. Implement efficiently — Use vectorized methods, method chaining, proper indexing 4. Validate results — Check dtypes, shapes, null counts, and row counts: 5. Optimize — Profile memory, apply categorical types, use chunking if needed Reference Guide Load detailed guidance based on context: Topic Reference Load When DataFrame Operations references/dataframe operations.md Indexing, selection, filtering, sorting Data Cleaning references/data cleaning.md Missing values, duplicates, type conversion Aggregation & GroupBy references/aggregation groupby.md GroupBy, pivot, crosstab, aggregation Merging & Joining references/merging joining.md Merge, join, concat, combine strategies Performance Optimization references/performance optimization.md Memory usage, vectorization, chunking Code Patterns Vectorized Operations (before/after) Safe Subsetting with .copy() GroupBy Aggregation Merge with Validation Missing Value Handling Time Series Resampling Pivot Table Memory Optimization Constraints MUST DO Use vectorized operations instead of loops Set appropriate dtypes (categorical for low cardinality strings) Check memory usage with .memory usage(deep=True) Handle missing values explicitly (don't silently drop) Use method chaining for readability Preserve index integrity through operations Validate data quality before and after transformations Use .copy() when modifying subsets to avoid SettingWithCopyWarning MUST NOT DO Iterate over DataFrame rows with .iterrows() unless absolutely necessary Use chained indexing ( df['A']['B'] ) — use .loc[] or .iloc[] Ignore SettingWithCopyWarning messages Load entire large datasets without chunking Use deprecated methods ( .ix , .append() — use pd.concat() ) Convert to Python lists for operations possible in pandas Assume data is clean without validation Output Templates When implementing pandas solutions, provide: 1. Code with vectorized operations and proper indexing 2. Comments explaining complex transformations 3. Memory/performance considerations if dataset is large 4. Data validation checks (dtypes, nulls, shapes) [Documentation](https://jeffallan.github.io/claude skills/skills/data ml/pandas pro/)

pandas-pro