pandas.json_normalize¶
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 , 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.
Nested records will generate names separated by sep. e.g., for sep=’.’, > -> foo.bar.
max_level int, default None
Max number of levels(depth of dict) to normalize. if None, normalizes all levels.
>>> 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 1 NaN Mose Reg 2 2.0 Faye Raker
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.
© Copyright 2008-2020, the pandas development team.
Created using Sphinx 3.3.1.
pandas.json_normalize#
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 , 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.
Nested records will generate names separated by sep. e.g., for sep=’.’, > -> foo.bar.
max_level int, default None
Max number of levels(depth of dict) to normalize. if None, normalizes all levels.
Returns : frame DataFrame Normalize semi-structured JSON data into a flat table.
>>> data = [ . "id": 1, "name": "first": "Coleen", "last": "Volk">>, . "name": "given": "Mark", "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 Mark Regner NaN 2 2.0 NaN NaN NaN NaN Faye Raker
>>> data = [ . . "id": 1, . "name": "Cole Volk", . "fitness": "height": 130, "weight": 60>, . >, . "name": "Mark 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 1 NaN Mark Reg 2 2.0 Faye Raker
Normalizes nested data up to level 1.
>>> data = [ . . "id": 1, . "name": "Cole Volk", . "fitness": "height": 130, "weight": 60>, . >, . "name": "Mark 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 Mark 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.
pandas.json_normalize#
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 , 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.
Nested records will generate names separated by sep. e.g., for sep=’.’, > -> foo.bar.
max_level int, default None
Max number of levels(depth of dict) to normalize. if None, normalizes all levels.
Returns : frame DataFrame Normalize semi-structured JSON data into a flat table.
>>> data = [ . "id": 1, "name": "first": "Coleen", "last": "Volk">>, . "name": "given": "Mark", "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 Mark Regner NaN 2 2.0 NaN NaN NaN NaN Faye Raker
>>> data = [ . . "id": 1, . "name": "Cole Volk", . "fitness": "height": 130, "weight": 60>, . >, . "name": "Mark 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 1 NaN Mark Reg 2 2.0 Faye Raker
Normalizes nested data up to level 1.
>>> data = [ . . "id": 1, . "name": "Cole Volk", . "fitness": "height": 130, "weight": 60>, . >, . "name": "Mark 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 Mark 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.