Pandas Pro

---
name: pandas-pro
description: "Use when manipulating DataFrames with pandas in Python: cleaning, NaN handling, groupby and pivot aggregation, merges and joins, and time-series resampling. Stresses vectorized operations, safe .loc subsetting, dtype and memory optimization, and validating shapes and nulls after each transform."
tags: ["data","python","analytics","performance"]
tools: []
license: MIT
source: https://github.com/Jeffallan/claude-skills
source_path: "skills/pandas-pro"
source_commit: 882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf
---
# 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` | Merge, join, concat, combine strategies |
| Performance Optimization | `references/performance-optimization.md` | Memory usage, vectorization, chunking |

## Code Patterns

### Vectorized Operations (before/after)

```python
# ❌ AVOID: row-by-row iteration
for i, row in df.iterrows():
    df.at[i, 'tax'] = row['price'] * 0.2

# ✅ USE: vectorized assignment
df['tax'] = df['price'] * 0.2
```

### Safe Subsetting with `.copy()`

```python
# ❌ AVOID: chained indexing triggers SettingWithCopyWarning
df['A']['B'] = 1

# ✅ USE: .loc[] with explicit copy when mutating a subset
subset = df.loc[df['status'] == 'active', :].copy()
subset['score'] = subset['score'].fillna(0)
```

### GroupBy Aggregation

```python
summary = (
    df.groupby(['region', 'category'], observed=True)
    .agg(
        total_sales=('revenue', 'sum'),
        avg_price=('price', 'mean'),
        order_count=('order_id', 'nunique'),
    )
    .reset_index()
)
```

### Merge with Validation

```python
merged = pd.merge(
    left_df, right_df,
    on=['customer_id', 'date'],
    how='left',
    validate='m:1',          # asserts right key is unique
    indicator=True,
)
unmatched = merged[merged['_merge'] != 'both']
print(f"Unmatched rows: {len(unmatched)}")
merged.drop(columns=['_merge'], inplace=True)
```

### Missing Value Handling

```python
# Forward-fill then interpolate numeric gaps
df['price'] = df['price'].ffill().interpolate(method='linear')

# Fill categoricals with mode, numerics with median
for col in df.select_dtypes(include='object'):
    df[col] = df[col].fillna(df[col].mode()[0])
for col in df.select_dtypes(include='number'):
    df[col] = df[col].fillna(df[col].median())
```

### Time Series Resampling

```python
daily = (
    df.set_index('timestamp')
    .resample('D')
    .agg({'revenue': 'sum', 'sessions': 'count'})
    .fillna(0)
)
```

### Pivot Table

```python
pivot = df.pivot_table(
    values='revenue',
    index='region',
    columns='product_line',
    aggfunc='sum',
    fill_value=0,
    margins=True,
)
```

### Memory Optimization

```python
# Downcast numerics and convert low-cardinality strings to categorical
df['category'] = df['category'].astype('category')
df['count'] = pd.to_numeric(df['count'], downcast='integer')
df['score'] = pd.to_numeric(df['score'], downcast='float')
print(df.memory_usage(deep=True).sum() / 1e6, "MB after 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/)