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Module « pandas »

Fonction json_normalize - module pandas

Signature de la fonction json_normalize

def json_normalize(data: 'dict | list[dict]', record_path: 'str | list | None' = None, meta: 'str | list[str | list[str]] | None' = None, meta_prefix: 'str | None' = None, record_prefix: 'str | None' = None, errors: 'IgnoreRaise' = 'raise', sep: 'str' = '.', max_level: 'int | None' = None) -> 'DataFrame' 

Description

help(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 : {'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.

Returns
-------
frame : DataFrame
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.

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