Convert CSV to Pydantic online, free. CSV is a flat, row/column tabular format every spreadsheet and database understands. Pydantic BaseModels validate and parse data into typed Python objects. CSV to Pydantic conversion parses your CSV against the RFC 4180 grammar (https://www.rfc-editor.org/rfc/rfc4180), builds an in-memory model of its keys, nested objects and arrays, then emits ready-to-use Pydantic types as Pydantic BaseModel classes with typed fields following the Pydantic docs conventions. Processing runs in your browser in JavaScript with no upload or server round-trip — no size limit beyond your device's memory, so multi-megabyte documents convert in milliseconds and sensitive payloads never leave your machine. Typical uses include FastAPI request/response models, settings validation and ETL parsing.
It maps the full structure of your CSV onto idiomatic Pydantic types, following the Pydantic docs conventions. 100% free, no registration, and complete privacy — everything runs locally in your browser, so your data never touches a server.
Paste CSV and generate Pydantic types from Pydantic immediately. Conversion runs client-side, so there is no upload wait and large documents stay fast.
Nested CSV objects, tables and arrays are mapped faithfully onto Pydantic, including nested types and optional fields.
Your CSV never leaves your device — everything is processed locally in JavaScript, with nothing logged or stored.
Unlimited conversions with no account, no quotas, and no watermark. Works on desktop and mobile.
A CSV document and its Pydantic equivalent:
CSV input:
name,age,active
Ada,36,trueOutput:
from pydantic import BaseModel
class Contact(BaseModel):
name: str
age: int
active: boolPaste your CSV into the editor and press "Convert to Pydantic". The tool parses it against the RFC 4180 grammar, then generates Pydantic types following Pydantic docs conventions — instantly and entirely in your browser. You can validate or format the CSV first to be sure it is clean.
CSV is flat and untyped: the header row supplies the field names and every column is read as text, so each row becomes one Pydantic record whose fields you may want to retype.
Optional or nullable members in the generated Pydantic are inferred from keys missing in some records of your CSV, following Pydantic docs. Include the optional fields in your sample so they are typed correctly.
CSV (Comma-Separated Values) (CSV) — CSV is a flat, row/column tabular format every spreadsheet and database understands. Pydantic models (Pydantic) — Pydantic BaseModels validate and parse data into typed Python objects. This converter maps the structure of your CSV onto Pydantic so you can use it for FastAPI request/response models.
Yes. Invalid CSV is flagged with a clear error before anything is converted. Common CSV problems to check are a consistent column count per row and proper quoting of fields that contain commas. Starting from clean input keeps the generated Pydantic accurate.
Yes. The entire CSV-to-Pydantic conversion runs locally in your browser in JavaScript — your CSV is never uploaded, logged or stored. That matters when the data is something like spreadsheet import/export, which should not leave your machine.
The generated Pydantic is ready for FastAPI request/response models, settings validation and ETL parsing. Copy or download it and drop it straight into your codebase.
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.
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