"no docstring" for pandas submodules?

I might have chosen unfortunately on the method/submodule: turns out the pandas.io.json.* is deprecated in favor of straight up pandas.* e.g. pandas.json_normalize. The docstring works fine from there:

pd.json_normalize?
Signature:
pd.json_normalize(
    data: Union[Dict, List[Dict]],
    record_path: Union[str, List, NoneType] = None,
    meta: Union[str, List[Union[str, List[str]]], NoneType] = None,
    meta_prefix: Optional[str] = None,
    record_prefix: Optional[str] = None,
    errors: str = 'raise',
    sep: str = '.',
    max_level: Optional[int] = None,
) -> 'DataFrame'
Docstring:
Normalize semi-structured JSON data into a flat table.
Parameters
----------
data : dict or list of dicts
    Unserialized JSON objects.
record_path : str or list of str, default None
    Path in each object to list of records. If not passed, data will be
    assumed to be an array of records.
meta : list of paths (str or list of str), default None
    Fields to use as metadata for each record in resulting table.
meta_prefix : str, default None
    If True, prefix records with dotted (?) path, e.g. foo.bar.field if
    meta is ['foo', 'bar'].
record_prefix : str, default None
    If True, prefix records with dotted (?) path, e.g. foo.bar.field if
    path to records is ['foo', 'bar'].
errors : {'raise', 'ignore'}, default 'raise'
    Configures error handling.
    * 'ignore' : will ignore KeyError if keys listed in meta are not
      always present.
    * 'raise' : will raise KeyError if keys listed in meta are not
      always present.
sep : str, default '.'
    Nested records will generate names separated by sep.
    e.g., for sep='.', {'foo': {'bar': 0}} -> foo.bar.
max_level : int, default None
    Max number of levels(depth of dict) to normalize.
    if None, normalizes all levels.
    .. versionadded:: 0.25.0
Returns
-------
frame : DataFrame
Normalize semi-structured JSON data into a flat table.
Examples
--------
 data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
         {'name': {'given': 'Mose', 'family': 'Regner'}},
         {'id': 2, 'name': 'Faye Raker'}]
 pd.json_normalize(data)
    id name.first name.last name.given name.family        name
0  1.0     Coleen      Volk        NaN         NaN         NaN
1  NaN        NaN       NaN       Mose      Regner         NaN
2  2.0        NaN       NaN        NaN         NaN  Faye Raker
 data = [{'id': 1,
          'name': "Cole Volk",
          'fitness': {'height': 130, 'weight': 60}},
         {'name': "Mose Reg",
          'fitness': {'height': 130, 'weight': 60}},
         {'id': 2, 'name': 'Faye Raker',
          'fitness': {'height': 130, 'weight': 60}}]
 pd.json_normalize(data, max_level=0)
    id        name                        fitness
0  1.0   Cole Volk  {'height': 130, 'weight': 60}
1  NaN    Mose Reg  {'height': 130, 'weight': 60}
2  2.0  Faye Raker  {'height': 130, 'weight': 60}
Normalizes nested data up to level 1.
 data = [{'id': 1,
          'name': "Cole Volk",
          'fitness': {'height': 130, 'weight': 60}},
         {'name': "Mose Reg",
          'fitness': {'height': 130, 'weight': 60}},
         {'id': 2, 'name': 'Faye Raker',
          'fitness': {'height': 130, 'weight': 60}}]
 pd.json_normalize(data, max_level=1)
    id        name  fitness.height  fitness.weight
0  1.0   Cole Volk             130              60
1  NaN    Mose Reg             130              60
2  2.0  Faye Raker             130              60
 data = [{'state': 'Florida',
          'shortname': 'FL',
          'info': {'governor': 'Rick Scott'},
          'counties': [{'name': 'Dade', 'population': 12345},
                       {'name': 'Broward', 'population': 40000},
                       {'name': 'Palm Beach', 'population': 60000}]},
         {'state': 'Ohio',
          'shortname': 'OH',
          'info': {'governor': 'John Kasich'},
          'counties': [{'name': 'Summit', 'population': 1234},
                       {'name': 'Cuyahoga', 'population': 1337}]}]
 result = pd.json_normalize(data, 'counties', ['state', 'shortname',
                                            ['info', 'governor']])
 result
         name  population    state shortname info.governor
0        Dade       12345   Florida    FL    Rick Scott
1     Broward       40000   Florida    FL    Rick Scott
2  Palm Beach       60000   Florida    FL    Rick Scott
3      Summit        1234   Ohio       OH    John Kasich
4    Cuyahoga        1337   Ohio       OH    John Kasich
 data = {'A': [1, 2]}
 pd.json_normalize(data, 'A', record_prefix='Prefix.')
    Prefix.0
0          1
1          2
Returns normalized data with columns prefixed with the given string.
File:      /usr/local/lib/python3.10/site-packages/pandas/io/json/_normalize.py
Type:      function