pandas.io.json.build_table_schema

pandas.io.json.build_table_schema(data, index=True, primary_key=None, version=True)

Create a Table schema from data.

This method is a utility to generate a JSON-serializable schema representation of a pandas Series or DataFrame, compatible with the Table Schema specification. It enables structured data to be shared and validated in various applications, ensuring consistency and interoperability.

Parameters:

dataSeries or DataFrame

The input data for which the table schema is to be created.

indexbool, default True

Whether to include data.index in the schema.

primary_keybool or None, default True

Column names to designate as the primary key. The default None will set ‘primaryKey’ to the index level or levels if the index is unique.

versionbool, default True

Whether to include a field pandas_version with the version of pandas that last revised the table schema. This version can be different from the installed pandas version.

Returns:

dict

A dictionary representing the Table schema.

See also

DataFrame.to_json

Convert the object to a JSON string.

read_json

Convert a JSON string to pandas object.

Notes

See Table Schema for conversion types. Timedeltas as converted to ISO8601 duration format with 9 decimal places after the seconds field for nanosecond precision.

Categoricals are converted to the any dtype, and use the enum field constraint to list the allowed values. The ordered attribute is included in an ordered field.

Examples

>>> from pandas.io.json._table_schema import build_table_schema
>>> df = pd.DataFrame(
...     {'A': [1, 2, 3],
...      'B': ['a', 'b', 'c'],
...      'C': pd.date_range('2016-01-01', freq='D', periods=3),
...      }, index=pd.Index(range(3), name='idx'))
>>> build_table_schema(df)
{'fields': [{'name': 'idx', 'type': 'integer'}, {'name': 'A', 'type': 'integer'}, {'name': 'B', 'type': 'string'}, {'name': 'C', 'type': 'datetime'}], 'primaryKey': ['idx'], 'pandas_version': '1.4.0'}