pandas-pro

1# Pandas Pro
2 
3Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns.
4 
5## Core Workflow
6 
71. **Assess data structure** — Examine dtypes, memory usage, missing values, data quality:
8   ```python
9   print(df.dtypes)
10   print(df.memory_usage(deep=True).sum() / 1e6, "MB")
11   print(df.isna().sum())
12   print(df.describe(include="all"))
13   ```
142. **Design transformation** — Plan vectorized operations, avoid loops, identify indexing strategy
153. **Implement efficiently** — Use vectorized methods, method chaining, proper indexing
164. **Validate results** — Check dtypes, shapes, null counts, and row counts:
17   ```python
18   assert result.shape[0] == expected_rows, f"Row count mismatch: {result.shape[0]}"
19   assert result.isna().sum().sum() == 0, "Unexpected nulls after transform"
20   assert set(result.columns) == expected_cols
21   ```
225. **Optimize** — Profile memory, apply categorical types, use chunking if needed
23 
24## Reference Guide
25 
26Load detailed guidance based on context:
27 
28| Topic | Reference | Load When |
29|-------|-----------|-----------|
30| DataFrame Operations | `references/dataframe-operations.md` | Indexing, selection, filtering, sorting |
31| Data Cleaning | `references/data-cleaning.md` | Missing values, duplicates, type conversion |
32| Aggregation & GroupBy | `references/aggregation-groupby.md` | GroupBy, pivot, crosstab, aggregation |
33…