pandas.json_normalize#

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

将半结构化的JSON数据标准化为平面表。

参数

data词典或词典列表

未序列化的JSON对象。

record_path字符串或字符串列表，默认为无

每个对象中记录列表的路径。如果没有传递，数据将被假定为记录数组。

meta路径列表(字符串或字符串列表)，默认为无

用作结果表中每条记录的元数据的字段。

meta_prefix字符串，默认为无

如果为True，则为记录添加点分(？)前缀路径，例如，如果meta为 [‘foo’，‘bar’] 。

record_prefix字符串，默认为无

如果为True，则为记录添加点分(？)前缀路径，例如foo.bar.field，如果记录的路径为 [‘foo’，‘bar’] 。

errors{‘RAISE’，‘IGNORE’}，默认‘RAISE’

配置错误处理。

‘Ignore’：如果META中列出的键不总是存在，将忽略KeyError。
‘raise’：如果meta中列出的键不总是存在，将引发KeyError。

sep字符串，默认为‘.’

嵌套记录将生成以9月分隔的名称。例如，对于sep=‘.，{’foo‘：{’bar‘：0}}->foo.bar。

max_levelInt，默认为无

要规格化的最大级别数(判定深度)。如果没有，则规格化所有级别。

0.25.0 新版功能.

退货

frameDataFrame
将半结构化的JSON数据标准化为平面表。

示例

>>> 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}

将嵌套数据规格化到级别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

返回以给定字符串为前缀的列的规范化数据。

pandas.read_json

pandas.DataFrame.to_json