pandas.io.json.json_normalize

pandas.io.json.json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None, errors='raise', sep='.')[source]

“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

meta_prefix : string, default None

record_prefix : string, 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’

  • ‘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

New in version 0.20.0.

sep : string, default ‘.’

Nested records will generate names separated by sep, e.g., for sep=’.’, { ‘foo’ : { ‘bar’ : 0 } } -> foo.bar

New in version 0.20.0.

Returns:
frame : DataFrame

Examples

>>> from pandas.io.json import json_normalize
>>> data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
...         {'name': {'given': 'Mose', 'family': 'Regner'}},
...         {'id': 2, 'name': 'Faye Raker'}]
>>> json_normalize(data)
    id        name name.family name.first name.given name.last
0  1.0         NaN         NaN     Coleen        NaN      Volk
1  NaN         NaN      Regner        NaN       Mose       NaN
2  2.0  Faye Raker         NaN        NaN        NaN       NaN
>>> 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 = json_normalize(data, 'counties', ['state', 'shortname',
...                                           ['info', 'governor']])
>>> result
         name  population info.governor    state shortname
0        Dade       12345    Rick Scott  Florida        FL
1     Broward       40000    Rick Scott  Florida        FL
2  Palm Beach       60000    Rick Scott  Florida        FL
3      Summit        1234   John Kasich     Ohio        OH
4    Cuyahoga        1337   John Kasich     Ohio        OH
>>> data = {'A': [1, 2]}
>>> json_normalize(data, 'A', record_prefix='Prefix.')
    Prefix.0
0          1
1          2
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