Why convert DTA to CSV?
A Stata .dta file is a proprietary binary statistical dataset understood by only a handful of programs, while .CSV opens in Excel, Google Sheets, every database import wizard, and virtually any data tool. Converting to CSV is the standard step for sharing a Stata dataset with collaborators who do not have a Stata licence.
How to convert DTA to CSV
Python + pandas OPEN-SOURCE
Install pandas and pyreadstat, then load with pd.read_stata('file.dta') and write with .to_csv('output.csv', index=False); add convert_categoricals=True to export value labels as readable text.
R + haven OPEN-SOURCE
Install haven, run read_dta('file.dta') to load the data, then write with write_csv() from readr; call as_factor(df) first to decode value labels to text.
Stata APP
Open the .dta in Stata and run the export delimited command in the Command window to write the CSV.
About these formats
.DTA
Stata Data File
A DTA file is almost always a Stata dataset - the native binary format of the StataCorp Stata statistics package. You do not need a paid Stata licence to open one: PondPilot opens it free…
Open .DTA details →
.CSV
Comma-Separated Values
A CSV file stores tabular data as plain text - one row per line, values separated by commas (or semicolons in European locales). Any spreadsheet opens it: Excel, Google Sheets, LibreOffice…
Open .CSV details →Quality & what to watch
- Categorical columns store numeric codes internally; without decoding they export as numbers rather than text labels - use
convert_categoricals=Truein pandas oras_factor()in R to fix this. - Variable labels (long descriptions) are metadata not supported by CSV and are dropped in the output.
- PondPilot reads Stata formats 8 through 19, covering all versions from Stata 8 to Stata 19.
Frequently asked questions
Do I need a Stata licence to convert DTA to CSV?
Why do my categorical columns show numbers instead of labels in the CSV?
convert_categoricals=True in pandas or as_factor() in R to decode them to text before exporting.
