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name: pandas-pro description: 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 time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets. license: MIT metadata: author: https://github.com/Jeffallan version: "1.1.0" domain: data-ml triggers: pandas, DataFrame, data manipulation, data cleaning, aggregation, groupby, merge, join, time series, data wrangling, pivot table, data transformation role: expert scope: implementation output-format: code related-skills: python-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:

   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:

   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)

# ❌ 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()

# ❌ 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

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

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

# 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

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

Pivot Table

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

Memory Optimization

# 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