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"