Convert Excel to SQLite3

Working from an Excel file

"Excel" covers two entirely different file formats, and which one you have decides how much type information survives the conversion. It is worth checking what your workbook was actually saved as before you upload it.

• →Only .xls and .xlsx are recognised. A macro-enabled .xlsm, or a workbook saved as OpenDocument .ods, is refused before anything is read - save a copy as .xlsx first.
• →The two extensions take different code paths inside the reader: .xlsx through openpyxl, .xls through pandas and xlrd, and they do not recover the same amount of information from the file.
• →Sheet names become table names with every character outside letters, digits and underscores replaced one for one, so "Q1 Sales (2024)" arrives as Q1_Sales__2024: the space and the opening bracket each leave an underscore behind, and the trailing one is trimmed.

The .sqlite3 name and what carries over

The 3 in sqlite3 is the file format version and the name of the command-line tool, not a property of your conversion. One database file arrives, and the two things worth knowing about it are what it is called and what it leaves behind.

• →The file inside the zip is always database.sqlite, whichever spelling brought you to this page. Rename it to database.sqlite3, or to whatever a framework expects, once you have it - the header is what identifies it, never the extension.
• →An auto-increment column arrives holding all of its values and nothing that assigns new ones. No AUTOINCREMENT keyword is written, and the column is declared BIGINT rather than exactly INTEGER, so it is not an alias for the rowid either: rows you insert later have to supply their own key.
• →Only the rows are transactional. The very first CREATE TABLE runs in autocommit, and everything after it - that table's rows, and every later table - sits in one transaction that commits after the last row. A conversion that fails partway therefore leaves a file holding an empty shell of the first table and nothing else. You never receive that file: the zip is only assembled once the writer has returned.

Reading Excel

• →Numbers, dates and booleans are read as Excel stored them, never converted to text and re-parsed.
• →Cell number formats are read out of .xlsx workbooks, so a column formatted as currency becomes a fixed-point decimal rather than a floating-point number — which is how money avoids acquiring a rounding tail. Legacy .xls is the exception: the xlrd path exposes no formats, so its currency columns come across as ordinary numbers.
• →A column formatted as a date becomes a date, not a timestamp at midnight; one formatted with a time keeps its time.
• →One sheet becomes one table. Empty sheets are skipped, and sheet names are sanitised for the target.

Writing SQLite3

• →Columns get real declared types rather than everything being TEXT.
• →Primary keys and NOT NULL constraints are preserved.
• →A money column is declared NUMERIC(10,2), but that declaration does not stop SQLite’s own numeric affinity from converting the inserted value into a float once it lands, so 19.99 can come back with a rounding tail. Pick the DuckDB target instead when the exact decimal has to survive.

Convert Excel to something else

• MySQL
• PostgreSQL
• Oracle
• SQL Server
• DuckDB
• CSV

Convert something else to SQLite3

• CSV
• SQL dump
• DBF
• JSON
• XML
• Parquet
• DuckDB
• MS Access
• PostgreSQL backup