Putting It All Together

You've built every layer:

Beyond:

• Type inference: pandas guesses column types from sample rows. Often wrong; use explicit dtypes.
• Schema validation: pandera, great_expectations check constraints.
• Streaming with mmap: for max throughput, mmap + pointer arithmetic. xsv does this.
• Compressed CSV: gzip-streamed CSVs are common (zcat | head | csvtool).
• Apache Arrow: in-memory columnar format. Faster for analytics; CSV → Arrow → Parquet pipelines are standard.

Use cases:

• Data import/export between systems
• Spreadsheets (Excel, Google Sheets, Numbers)
• Database dumps (COPY TO in PostgreSQL)
• Machine learning datasets
• Configuration files (rare but happens)

CSV is INFURIATINGLY common. Knowing the edge cases saves you from data corruption nightmares.