CSV to Pydantic conversion turns the columns of a CSV file into a typed Pydantic BaseModel describing one row. Each header becomes a field, and the converter infers the field type from the column values — whole numbers become int, decimals become float, true/false becomes bool, and the rest stay str. The result is a validation-ready model, perfect for loading and checking CSV data in data and ETL pipelines.
CSV cells are untyped text, so the tool scans all rows of each column and assigns the narrowest fitting Python type:
true or false (any case).Validate a CSV import row-by-row, build a typed model for a data-ingestion or ETL job, parse spreadsheet exports into Pydantic objects, or document the expected shape of a dataset — all in your browser, with nothing uploaded.
It infers types. Fields become int, float or bool when every value in the column qualifies; otherwise the field is str.
One row. Validate each parsed CSV record against the model, e.g. [Row(**r) for r in reader].
The generated model uses standard BaseModel field syntax that works with both Pydantic v1 and v2; adjust validators or config to your version if needed.
No. Conversion runs entirely in your browser — your data never leaves your device.