pandas-pro

# 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: ```python print(df.dtypes) print(df.memory_usage(deep=True).sum() / 1e6, "MB") print(df.isna().sum()) print(df.describe(include="all")) ``` 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: ```python assert result.shape[0] == expected_rows, f"Row count mismatch: {result.shape[0]}" assert result.isna().sum().sum() == 0, "Unexpected nulls after transform" assert set(result.columns) == expected_cols ``` 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` ...