Production checklist for Pandas

Production Pandas pipelines need dtype discipline, explicit missing-data policies, merge validation, reproducible transforms, and efficient IO—especially before ML serving or SQL warehouse handoff.

Before shipping analytics code

• Assert expected columns and dtypes after every load/merge
• Document imputation and outlier rules
• Use validate= on merges; check row count inflation
• Prefer Parquet for intermediate artifacts
• Version control transformation code—not notebook-only clicks
• Log shape and null summary at pipeline stages

Testing

import pandas as pd
expected_cols = {'id', 'amount', 'date'}
df = pd.DataFrame({'id': [1], 'amount': [10.0],
                   'date': pd.to_datetime(['2024-01-01'])})
assert expected_cols <= set(df.columns)
assert df['amount'].dtype == float
print('schema OK')

Scale limits

When data exceeds RAM, push aggregation to SQL, use chunked read_csv(chunksize=), or migrate to Polars/DuckDB/Spark. Pandas remains the lingua franca for moderate-scale Python ETL.

Important interview questions and answers

1. Q: validate='one_to_many'?
   A: Asserts merge keys are unique on left—catches accidental row explosions.
2. Q: Parquet in prod?
   A: Preserves dtypes, faster reloads, smaller storage than CSV in pipelines.

Self-check

1. List five production Pandas checklist items.
2. Why assert schema after load?
3. When move aggregation to SQL?

Tip: Assert column set and dtypes at every pipeline stage boundary.