metasyn.MetaFrame

class metasyn.MetaFrame(meta_vars, n_rows=None, file_format=None, name='single_table')

Container for statistical metadata describing a dataset.

This class is used to fit a MetaFrame to a Polars DataFrame, serialize and save the MetaFrame to a file, read a MetaFrame from a file, and create a synthetic Polars DataFrame.

A MetaFrame represents a metadata frame, which is a structure that holds statistical metadata about a dataset. The data contained in a MetaFrame follows the Generative Metadata Format (GMF). The metadata is contained in a collection of MetaVar objects, with each MetaVar representing a column (variable).

A MetaFrame can easily be created using the fit_dataframe method, which takes a Polars DataFrame and fits a MetaFrame to it.

Parameters:

  • meta_vars (List[MetaVar]) – List of variables representing columns in a DataFrame.

  • n_rows (Optional[int]) – Number of rows in the original DataFrame.

  • privacy_package – Package that supplies the distributions.

  • file_format (Union[None, BaseFileInterface, dict[str, Any]])

  • name (str)

__init__(meta_vars, n_rows=None, file_format=None, name='single_table')
Parameters:

  • meta_vars (List[MetaVar])

  • n_rows (int | None)

  • file_format (None | BaseFileInterface | dict[str, Any])

  • name (str)

Methods

__init__(meta_vars[, n_rows, file_format, name])

export(fp[, validate])

Export, deprecated method, use Metaframe.save instead.

fit_dataframe(df[, var_specs, plugins, ...])

Create a metasyn object from a polars (or pandas) dataframe.

from_config(meta_config)

Create a MetaFrame using a configuration, but without a DataFrame.

from_dict(gmf_dict[, table_name])

from_json(fp[, validate])

Import, deprecated method, use Metaframe.load_json instead.

load(fp[, validate, table_name])

Read a MetaFrame from a JSON or TOML GMF file.

load_json(fp[, validate, table_name])

Read a MetaFrame from a JSON file.

load_toml(fp[, validate, table_name])

save(fp[, validate])

Serialize and save the MetaFrame to a JSON or TOML file, following the GMF format.

save_json(fp[, validate])

Serialize and save the MetaFrame to a JSON file, following the GMF format.

save_toml(fp[, validate])

synthesize([n, seed, progress_bar])

Create a synthetic Polars dataframe.

to_dict()

Create dictionary with the properties for recreation.

to_json(fp[, validate])

Export, deprecated method, use Metaframe.save_json instead.

write_synthetic([file_name, n, seed, ...])

Write a synthetic dataset to a file.

Attributes

descriptions

Return the descriptions of the columns.

file_format

n_columns

Number of columns of the original dataframe.