CSV: simple tables with hidden assumptions
Learn why comma-separated data is easy to exchange but still needs a clear agreement about columns, types, encoding, and coordinates.
Why this matters
CSV is common because people can create it with spreadsheets and software can parse it easily. Its simplicity is also its weakness: the file usually carries little information about what each column means.
Learn it
A header row commonly names columns and later rows carry values. The delimiter may be a comma or another character depending on the convention. Types, units, missing values, date formats, coordinate order, encoding, and quoting rules need to be agreed outside the raw cells. A schema or import mapping makes those assumptions explicit.
Work through an example
A latitude and longitude column can describe points, but the file still needs to say whether time is UTC, whether temperature is Celsius, and what blank cells mean.
station_id,longitude,latitude,temp_c
A-01,-122.3321,47.6062,18.4
A-02,-122.3308,47.6071,18.1Practice
- 1Read the sample and list every assumption you would need before importing it safely.
- 2Add an observation time and units column.
- 3Decide which columns become geometry and which remain properties.
What to watch for
Never infer units or coordinate order from a column name when the source specification can tell you directly.
Where this takes you
CSV is useful for field exports, spreadsheet exchange, bulk imports, reference tables, and simple observations.