human
What is a Python program to fix : string, default None given the following:
Complete the below
def json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None, errors='raise', sep='.'):

    ''''"Normalize" semi-structured JSON data into a flat table
    Parameters
    data : dict or list of dicts
    Unserialized JSON objects
    record_path : string or list of strings, 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 (string or list of strings), default None
    Fields to use as metadata for each record in resulting table
    record_prefix : string, default None
    If True, prefix records with dotted (?) path, e.g. foo.bar.field if
    path to records is [\'foo\', \'bar\']
    meta_prefix : string, default None
    errors : {\'raise\', \'ignore\'}, default \'raise\'
    * \'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
    .. versionadded:: 0.20.0
    sep : string, default \'.\'
    Nested records will generate names separated by sep,
    e.g., for sep=\'.\', { \'foo\' : { \'bar\' : 0 } } -> foo.bar
    .. versionadded:: 0.20.0
    Returns
    frame : DataFrame
    Examples
    >>> 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}]}]
    >>> from pandas.io.json import json_normalize
    >>> result = json_normalize(data, \'counties\', [\'state\', \'shortname\',
    ...                                           [\'info\', \'governor\']])
 ...'''
assistant
#Here is Python program to fix : string, default None
.
    def _pull_field(js, spec):
        result = js
        if isinstance(spec, list):
            for field in spec:
                result = result[field]
        else:
            result = result[spec]
        return result
    if (isinstance(data, list) and (len(data) is 0)):
        return DataFrame()
    if isinstance(data, dict):
        data = [data]
    if (record_path is None):
        if any([isinstance(x, dict) for x in compat.itervalues(data[0])]):
            data = nested_to_record(data, sep=sep)
        return DataFrame(data)
    elif (not isinstance(record_path, list)):
        record_path = [record_path]
    if (meta is None):
        meta = []
    elif (not isinstance(meta, list)):
        meta = [meta]
    for (i, x) in enumerate(meta):
        if (not isinstance(x, list)):
            meta[i] = [x]
    records = []
    lengths = []
    meta_vals = defaultdict(list)
    if (not isinstance(sep, compat.string_types)):
        sep = str(sep)
    meta_keys = [sep.join(val) for val in meta]
    def _recursive_extract(data, path, seen_meta, level=0):
        if (len(path) > 1):
            for obj in data:
                for (val, key) in zip(meta, meta_keys):
                    if ((level + 1) == len(val)):
                        seen_meta[key] = _pull_field(obj, val[(-1)])
                _recursive_extract(obj[path[0]], path[1:], seen_meta, level=(level + 1))
        else:
            for obj in data:
                recs = _pull_field(obj, path[0])
                lengths.append(len(recs))
                for (val, key) in zip(meta, meta_keys):
                    if ((level + 1) > len(val)):
                        meta_val = seen_meta[key]
                    else:
                        try:
                            meta_val = _pull_field(obj, val[level:])
                        except KeyError as e:
                            if (errors == 'ignore'):
                                meta_val = np.nan
                            else:
                                raise...