pandas-pro

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 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" )) Design transformation — Plan vectorized operations, avoid loops, identify indexing strategy Implement efficiently — Use vectorized methods, method chaining, proper indexing 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 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: Code with vectorized operations and proper indexing Comments explaining complex transformations Memory/performance considerations if dataset is large Data validation checks (dtypes, nulls, shapes) Documentation