pandas-pro
Expert pandas data manipulation with vectorized operations, memory optimization, and production-grade validation patterns.
- 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
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"
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