pandas.json_normalize — pandas 2.2.3 documentation (original) (raw)

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

Normalize semi-structured JSON data into a flat table.

Parameters:

datadict or list of dicts

Unserialized JSON objects.

record_pathstr 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.

metalist of paths (str or list of str), default None

Fields to use as metadata for each record in resulting table.

meta_prefixstr, default None

If True, prefix records with dotted (?) path, e.g. foo.bar.field if meta is [‘foo’, ‘bar’].

record_prefixstr, 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.

sepstr, default ‘.’

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

max_levelint, default None

Max number of levels(depth of dict) to normalize. if None, normalizes all levels.

Returns:

frameDataFrame

Normalize semi-structured JSON data into a flat table.

Examples

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 {'height': 130, 'weight': 60} 1 NaN Mark 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": "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.