DataFrame.to_stata(path, convert_dates=None, write_index=True, byteorder=None, time_stamp=None, data_label=None, variable_labels=None, version=114, convert_strl=None, compression='infer', storage_options=None)[source]

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. “dta” files contain a Stata dataset.

pathstr, buffer or path object

String, path object (pathlib.Path or py._path.local.LocalPath) or object implementing a binary write() function. If using a buffer then the buffer will not be automatically closed after the file data has been written.

Changed in version 1.0.0.

Previously this was “fname”


Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are ‘tc’, ‘td’, ‘tm’, ‘tw’, ‘th’, ‘tq’, ‘ty’. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to ‘tc’. Raises NotImplementedError if a datetime column has timezone information.


Write the index to Stata dataset.


Can be “>”, “<”, “little”, or “big”. default is sys.byteorder.


A datetime to use as file creation date. Default is the current time.

data_labelstr, optional

A label for the data set. Must be 80 characters or smaller.


Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller.

version{114, 117, 118, 119, None}, default 114

Version to use in the output dta file. Set to None to let pandas decide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.

Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.

Changed in version 1.0.0: Added support for formats 118 and 119.

convert_strllist, optional

List of column names to convert to string columns to Stata StrL format. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output dta. If string, specifies compression mode. If dict, value at key ‘method’ specifies compression mode. Compression mode must be one of {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and fname is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, or ‘.xz’ (otherwise no compression). If dict and compression mode is one of {‘zip’, ‘gzip’, ‘bz2’}, or inferred as one of the above, other entries passed as additional compression options.

New in version 1.1.0.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.

New in version 1.2.0.

  • If datetimes contain timezone information

  • Column dtype is not representable in Stata

  • Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime

  • Column listed in convert_dates is not in DataFrame

  • Categorical label contains more than 32,000 characters

See also


Import Stata data files.


Low-level writer for Stata data files.


Low-level writer for version 117 files.


>>> df = pd.DataFrame({'animal': ['falcon', 'parrot', 'falcon',
...                               'parrot'],
...                    'speed': [350, 18, 361, 15]})
>>> df.to_stata('animals.dta')