ntv-pandas 1.1.1

NTV-pandas : A tabular analyzer and a semantic, compact and reversible JSON-pandas converter

NTV-pandas : A tabular analyzer and a semantic, compact and reversible JSON-pandas converter

For more information, see the user guide or the github repository.

NTV-pandas is referenced in the pandas ecosystem.

Why a NTV-pandas converter ?

pandas provide JSON converter but three limitations are present:

• the JSON-pandas converter take into account few data types,
• the JSON-pandas converter is not always reversible (conversion round trip)
• external data types (e.g. TableSchema types) are not included

pandas does not have a tool for analyzing tabular structures and detecting integrity errors

main features

The NTV-pandas converter uses the semantic NTV format to include a large set of data types in a JSON representation.

The converter integrates:

• all the pandas dtype and the data-type associated to a JSON representation,
• an always reversible conversion,
• a full compatibility with Table Schema specification

The NTV-pandas analyzer uses the TAB-analysis tool to analyze and measure the relationships between Fields in DataFrame and the TAB-dataset to identify integrity errors (example).

NTV-pandas was developped originally in the json-NTV project

converter example

In the example below, a DataFrame with multiple data types is converted to JSON (first to NTV format and then to Table Schema format).

The DataFrame resulting from these JSON conversions are identical to the initial DataFrame (reversibility).

With the existing JSON interface, these conversions are not possible.

Data example:

In [1]: from shapely.geometry import Point
        from datetime import date
        import pandas as pd
        import ntv_pandas as npd

In [2]: data = {'index':        [100, 200, 300, 400, 500],
                'dates::date':  [date(1964,1,1), date(1985,2,5), date(2022,1,21), date(1964,1,1), date(1985,2,5)],
                'value':        [10, 10, 20, 20, 30],
                'value32':      pd.Series([12, 12, 22, 22, 32], dtype='int32'),
                'res':          [10, 20, 30, 10, 20],
                'coord::point': [Point(1,2), Point(3,4), Point(5,6), Point(7,8), Point(3,4)],
                'names':        pd.Series(['john', 'eric', 'judith', 'mila', 'hector'], dtype='string'),
                'unique':       True }

In [3]: df = pd.DataFrame(data).set_index('index')
        df.index.name = None

In [4]: df
Out[4]:       dates::date  value  value32  res coord::point   names  unique
        100    1964-01-01     10       12   10  POINT (1 2)    john    True
        200    1985-02-05     10       12   20  POINT (3 4)    eric    True
        300    2022-01-21     20       22   30  POINT (5 6)  judith    True
        400    1964-01-01     20       22   10  POINT (7 8)    mila    True
        500    1985-02-05     30       32   20  POINT (3 4)  hector    True

JSON-NTV representation:

In [5]: df_to_json = df.npd.to_json()
        pprint(df_to_json, compact=True, width=120, sort_dicts=False)
Out[5]: {':tab': {'index': [100, 200, 300, 400, 500],
                  'dates::date': ['1964-01-01', '1985-02-05', '2022-01-21', '1964-01-01', '1985-02-05'],
                  'value': [10, 10, 20, 20, 30],
                  'value32::int32': [12, 12, 22, 22, 32],
                  'res': [10, 20, 30, 10, 20],
                  'coord::point': [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [3.0, 4.0]],
                  'names::string': ['john', 'eric', 'judith', 'mila', 'hector'],
                  'unique': True}}

Reversibility:

In [6]: print(npd.read_json(df_to_json).equals(df))
Out[6]: True

Table Schema representation:

In [7]: df_to_table = df.npd.to_json(table=True)
        pprint(df_to_table['data'][0], sort_dicts=False)
Out[7]: {'index': 100,
         'dates': '1964-01-01',
         'value': 10,
         'value32': 12,
         'res': 10,
         'coord': [1.0, 2.0],
         'names': 'john',
         'unique': True}

In [8]: pprint(df_to_table['schema'], sort_dicts=False)
Out[8]: {'fields': [{'name': 'index', 'type': 'integer'},
                    {'name': 'dates', 'type': 'date'},
                    {'name': 'value', 'type': 'integer'},
                    {'name': 'value32', 'type': 'integer', 'format': 'int32'},
                    {'name': 'res', 'type': 'integer'},
                    {'name': 'coord', 'type': 'geopoint', 'format': 'array'},
                    {'name': 'names', 'type': 'string'},
                    {'name': 'unique', 'type': 'boolean'}],
         'primaryKey': ['index'],
         'pandas_version': '1.4.0'}

Reversibility:

In [9]: print(npd.read_json(df_to_table).equals(df))
Out[9]: True