How are column types decided?

Every non-empty value in a column is checked: if all of them are whole numbers, the column is INTEGER; if all of them are numbers (allowing a decimal point or exponent), it's REAL; otherwise it's TEXT. An all-empty column defaults to TEXT.

What happens to blank cells?

A blank cell becomes NULL in the generated INSERT statement, regardless of the column's inferred type.

Are string values escaped safely?

Yes, any single quote inside a TEXT value is doubled ('' ) per standard SQL string-literal escaping, so values containing an apostrophe don't break the generated statement.

Is the generated SQL specific to one database engine?

No, it uses standard ANSI SQL syntax (double-quoted identifiers, INTEGER/REAL/TEXT types) that works as-is or with minor tweaks in SQLite, PostgreSQL, and most other engines; some databases (like MySQL) may need identifiers switched to backticks.

CSV to SQL (CREATE TABLE + INSERT) Converter

Overview

Introduction

Loading a CSV export into a database usually means writing a CREATE TABLE statement and a pile of INSERTs by hand. This tool generates both directly from your CSV, inferring column types automatically.

You provide a table name, and it scans every column's values to decide between INTEGER, REAL, or TEXT before generating the schema and data statements.

What Is CSV to SQL Converter?

A CSV-to-SQL converter that produces a CREATE TABLE statement (with an inferred type per column) followed by one INSERT INTO statement per data row.

It's a schema-and-data generator, not a database client: it produces SQL text for you to run yourself in whatever engine you're targeting.

How CSV to SQL Converter Works

For each column, every non-blank value across all rows is tested against integer and decimal-number patterns; the column's type is the most specific one that fits every value (INTEGER, then REAL, falling back to TEXT).

The CREATE TABLE statement lists each column with its inferred type; each INSERT INTO statement lists the same column names and one row's values, with blank cells becoming NULL and TEXT values single-quoted with internal quotes doubled.

When To Use CSV to SQL Converter

Use it when you need to load a CSV export into a fresh SQL database table and want the schema (types) generated for you instead of guessing at column definitions by hand.

It's also useful for quickly seeding a test database with sample data from a spreadsheet.

Often used alongside SQL INSERT to CSV Converter, CSV to LaTeX Table Converter and CSV to INI Converter.

Features

Advantages

Infers column types automatically instead of defaulting everything to TEXT.
Escapes string literals correctly, preventing broken SQL from a stray apostrophe in the data.
Runs entirely client-side, so the data never leaves your browser.

Limitations

Type inference only distinguishes INTEGER, REAL, and TEXT; it doesn't detect dates, booleans, or other richer types.
Identifier quoting uses standard double quotes, which some engines (notably MySQL) don't accept for identifiers by default and may need adjusting to backticks.

Examples

Generating SQL from a small CSV

Input

id,name,price
1,Widget,9.99
2,Gadget,19.5

Output

CREATE TABLE "products" (
  "id" INTEGER,
  "name" TEXT,
  "price" REAL
);

INSERT INTO "products" ("id", "name", "price") VALUES (1, 'Widget', 9.99);
INSERT INTO "products" ("id", "name", "price") VALUES (2, 'Gadget', 19.5);

id is inferred as INTEGER, price as REAL (since 9.99 isn't a whole number), and name stays TEXT and gets quoted.

Best Practices & Notes

Best Practices

Double-check the inferred types before running the SQL against a production database; a column meant to be TEXT (like a zip code) can be misinferred as INTEGER if every sampled value happens to look numeric.
Adjust identifier quoting (double quotes vs. backticks) to match your target database engine if needed.
Use SQL to CSV Converter afterward on the generated INSERT statements to sanity-check the round trip.

Developer Notes

Type inference scans every row's value for a column, not just the first one, deliberately: a column that looks like all integers in its first few rows but contains a single non-numeric value further down needs to fall back to TEXT for every row, not just the row where the mismatch appears.

CSV to SQL Converter Use Cases

Loading a CSV export into a new database table with an inferred schema
Seeding a test or development database with sample data from a spreadsheet
Quickly generating INSERT statements for a small dataset without writing them by hand

Common Mistakes

Trusting the inferred type for a column like a phone number or ZIP code that happens to look numeric but should really stay TEXT (e.g. to preserve a leading zero).
Running the generated SQL as-is against an engine with different identifier-quoting rules (like MySQL's backticks) without adjusting it first.

Tips

If a numeric-looking column should stay TEXT (like a ZIP code with a leading zero), edit the CREATE TABLE statement's type by hand after generating it.
Pair with SQL to CSV Converter to double-check the generated INSERT statements parse back into the data you expect.

CSV to SQL Converter