pyreadstat read and write spss without data loss

To read an spss .sav file using pandas/pyreadstat, you use:

df, meta = pyreadstat.read_sav()

to write a dataframe, you use:

pyreadstat.write_sav(df)

How can I read, edit and write a .sav file without losing any meta data, like labels and other things that can be changed in spss?

If this is not possible entirely, what would be the closest to not losing data this way?

Talk is cheap, here's the code. :-)

# using pyreadstat
from pyreadstat import write_sav

class TempFile(type(pathlib.Path())):  # type: ignore
    def __exit__(self, exc_type, exc_val, exc_tb):
        filepath = str(self.absolute())
        try:
            os.remove(filepath)
        except OSError:
            logger.exception('romve temporary file: %s failed!', filepath)
        self._closed = True

class SpssTool:
    @classmethod
    def to_spss(cls, df: DataFrame, io: BytesIO, metadata: metadata_container, *, compress: bool = False):
        """Writes a pandas dataframe to a BytesIO object.

        Parameters
        ----------
        df : pandas.DataFrame
            pandas data frame to write to sav or zsav
        io : BytesIO
            the buffer to save spss file
        metadata: metadata_container
            spss file meta data container
        compress : bool
            whether compress to zsav.
        """

        df.columns = SpssTool.get_legal_column_names(df.columns.to_list())

        with TempFile(f'/tmp/{uuid4().hex}.{"zsav" if compress else "sav"}') as fp:
            write_sav(
                df=df,
                dst_path=fp,
                column_labels=metadata.column_labels if metadata else None,
                variable_value_labels=dict(metadata.variable_value_labels) if metadata else {},
                variable_measure=metadata.variable_measure if metadata else None,
            )
            io.write(fp.read_bytes())

Some expalinations:

• SpssTool.get_legal_column_names

this is needed because spss file has restriction about the column name， see official document for details: https://www.ibm.com/docs/en/spss-statistics/27.0.0?topic=view-variable-names

• metadata_container

This is from from pyreadstat import metadata_container. the container holding info about the dataset, you could find more detail in : https://ofajardo.github.io/pyreadstat_documentation/_build/html/index.html#metadata-object-description

Those maybe what you need.