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Parquet conversion problem

Fix CSV-to-Parquet Schema and Type Errors

Prepare mixed CSV columns for Parquet conversion by defining stable types, null handling and validation checks.

Use the matching tool

CSV to Parquet

Open CSV to Parquet

What the error actually means

CSV fields begin as text, while Parquet stores a typed columnar schema. A column containing numbers, blanks and text markers cannot be safely assigned a numeric type without a rule. Type inference from an early sample can also fail on later rows.

Likely causes

  • A numeric column contains currency symbols or null markers.
  • Dates use mixed formats.
  • Identifiers were inferred as numbers.
  • Later records contain values outside the initially inferred type.

Stable column type

Problem

amount: 12.50, N/A, $9.00

Correct pattern

amount: 12.50, null, 9.00 after documented cleaning

A safe repair workflow

  1. 1Profile the complete column or a representative sample.
  2. 2Normalize nulls, numbers and dates before conversion.
  3. 3Keep identifiers as strings unless arithmetic is intended.
  4. 4Read the Parquet output and compare its schema and counts with the CSV.

How to verify the result

A file that downloads successfully is not automatically a correct file. Check the result at both the structural and business-data levels:

  • Row counts match.
  • Identifier values remain exact.
  • Null counts match the approved mapping.
  • The Parquet schema is stable across recurring files.

Read the deeper guides

Convert CSV to Parquet and Check the Schema Before You Trust ItA practical guide to converting CSV data to Parquet, preserving identifiers, reviewing inferred types, and validating the result in DuckDB or Python.How to Prepare CSV Files for Python PandasPrepare CSV files for reliable pandas imports by making encoding, delimiters, nulls, dates, identifiers, and column types explicit.

Official references

Platform requirements and technical standards can change. These are the primary references used for this page.