pandas-pro

Expert pandas data manipulation with vectorized operations, memory optimization, and production-grade validation patterns. Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns. Load detailed guidance based on context: When implementing pandas solutions, provide: Documentation - Covers core workflows: data assessment, transformation design, efficient implementation, result validation, and memory profiling - Includes reference guides and code patterns for DataFrame operations, data cleaning, aggregation, merging, and time series resampling - Enforces vectorized operations over iteration, proper indexing with .loc[] / .iloc[] , and explicit missing value handling - Provides memory optimization techniques including categorical type conversion, numeric downcasting, and chunking strategies for large datasets - 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,...