循环遍历 json 文件 python 以获取特定的键和值

Question

I have a json file built like this:

{"type":"FeatureCollection","features":[
{"type":"Feature","id":"010020000A0225","geometry":{"type":"Polygon","coordinates":[[[5.430767,46.0214267],[5.4310805,46.0220116],[5.4311205,46.0220864],[5.4312362,46.0223019],[5.4308994,46.0224141],[5.43087,46.0224242],[5.430774,46.0222401],[5.4304506,46.0223202],[5.4302885,46.021982],[5.4300391,46.0216054],[5.4299637,46.0216342],[5.4300862,46.0218401],[5.4299565,46.021902],[5.4298847,46.0218195],[5.4298545,46.0217829],[5.4297689,46.0216672],[5.4297523,46.0216506],[5.4297379,46.0216389],[5.4296432,46.0215854],[5.429517,46.0214509],[5.4294188,46.0213458],[5.4293757,46.0213128],[5.4291918,46.0211768],[5.4291488,46.0211448],[5.4291083,46.0211214],[5.429024,46.0210828],[5.4292965,46.0208202],[5.4294241,46.0208894],[5.4295183,46.0209623],[5.4295455,46.0209865],[5.429613,46.0210554],[5.4296428,46.0210813],[5.4298751,46.0212862],[5.429988,46.0213782],[5.430014,46.0213973],[5.4300746,46.0214318],[5.430124,46.0214542],[5.4302569,46.0215069],[5.4303111,46.0215192],[5.4303632,46.0215166],[5.4306127,46.0214642],[5.430767,46.0214267]]]},"properties":{"id":"010020000A0225","commune":"01002","prefixe":"000","section":"A","numero":"225","contenance":9440,"arpente":false,"created":"2005-06-03","updated":"2018-09-25"}},
{"type":"Feature","id":"010020000A0346","geometry":{"type":"Polygon","coordinates":[[[5.4241952,46.0255535],[5.4233594,46.0262031],[5.4232624,46.0262774],[5.4226259,46.0267733],[5.4227608,46.0268718],[5.4227712,46.0268789],[5.4226123,46.0269855],[5.422565,46.0270182],[5.4223546,46.027145],[5.4222957,46.0271794],[5.4221794,46.0272376],[5.4221383,46.0272585],[5.4221028,46.027152],[5.4220695,46.0270523],[5.4220378,46.026962],[5.4220467,46.0269265],[5.4220524,46.0268709],[5.4220563,46.0268474],[5.4222945,46.0268985],[5.4224161,46.0267746],[5.4224581,46.0267904],[5.4226286,46.02666],[5.4226811,46.02662],[5.4227313,46.0265803],[5.4227813,46.0265406],[5.4228535,46.0264868],[5.4229063,46.0264482],[5.4229741,46.0264001],[5.4234903,46.0260331],[5.4235492,46.0259893],[5.4235787,46.0259663],[5.423645,46.0259126],[5.4237552,46.0258198],[5.4237839,46.0257951],[5.4238321,46.0257547],[5.4239258,46.0256723],[5.4239632,46.0256394],[5.4241164,46.0255075],[5.4241952,46.0255535]]]},"properties":{"id":"010020000A0346","commune":"01002","prefixe":"000","section":"A","numero":"346","contenance":2800,"arpente":false,"created":"2005-06-03","updated":"2018-09-25"}},
   

I would like to get for each feature: properties and geometry but I think I loop badly on my json file. here is my code

data = pd.read_json(json_file_path)

for key, v in data.items():
    print(f"{key['features']['geometry']} : {v}",
          f"{key['features']['properties']} : {v}")

Answer 1

The values you are interested in are located in a list that is itself a value of your main dictionary. If you want to be able to process these values with pandas, it would be better to build your dataframe directly from them:

import json
import pandas as pd

data = json.loads("""{"type":"FeatureCollection","features":[
{"type":"Feature","id":"010020000A0225","geometry":{"type":"Polygon","coordinates":[[[5.430767,46.0214267],[5.4310805,46.0220116],[5.4311205,46.0220864],[5.4312362,46.0223019],[5.4308994,46.0224141],[5.43087,46.0224242],[5.430774,46.0222401],[5.4304506,46.0223202],[5.4302885,46.021982],[5.4300391,46.0216054],[5.4299637,46.0216342],[5.4300862,46.0218401],[5.4299565,46.021902],[5.4298847,46.0218195],[5.4298545,46.0217829],[5.4297689,46.0216672],[5.4297523,46.0216506],[5.4297379,46.0216389],[5.4296432,46.0215854],[5.429517,46.0214509],[5.4294188,46.0213458],[5.4293757,46.0213128],[5.4291918,46.0211768],[5.4291488,46.0211448],[5.4291083,46.0211214],[5.429024,46.0210828],[5.4292965,46.0208202],[5.4294241,46.0208894],[5.4295183,46.0209623],[5.4295455,46.0209865],[5.429613,46.0210554],[5.4296428,46.0210813],[5.4298751,46.0212862],[5.429988,46.0213782],[5.430014,46.0213973],[5.4300746,46.0214318],[5.430124,46.0214542],[5.4302569,46.0215069],[5.4303111,46.0215192],[5.4303632,46.0215166],[5.4306127,46.0214642],[5.430767,46.0214267]]]},"properties":{"id":"010020000A0225","commune":"01002","prefixe":"000","section":"A","numero":"225","contenance":9440,"arpente":false,"created":"2005-06-03","updated":"2018-09-25"}},
{"type":"Feature","id":"010020000A0346","geometry":{"type":"Polygon","coordinates":[[[5.4241952,46.0255535],[5.4233594,46.0262031],[5.4232624,46.0262774],[5.4226259,46.0267733],[5.4227608,46.0268718],[5.4227712,46.0268789],[5.4226123,46.0269855],[5.422565,46.0270182],[5.4223546,46.027145],[5.4222957,46.0271794],[5.4221794,46.0272376],[5.4221383,46.0272585],[5.4221028,46.027152],[5.4220695,46.0270523],[5.4220378,46.026962],[5.4220467,46.0269265],[5.4220524,46.0268709],[5.4220563,46.0268474],[5.4222945,46.0268985],[5.4224161,46.0267746],[5.4224581,46.0267904],[5.4226286,46.02666],[5.4226811,46.02662],[5.4227313,46.0265803],[5.4227813,46.0265406],[5.4228535,46.0264868],[5.4229063,46.0264482],[5.4229741,46.0264001],[5.4234903,46.0260331],[5.4235492,46.0259893],[5.4235787,46.0259663],[5.423645,46.0259126],[5.4237552,46.0258198],[5.4237839,46.0257951],[5.4238321,46.0257547],[5.4239258,46.0256723],[5.4239632,46.0256394],[5.4241164,46.0255075],[5.4241952,46.0255535]]]},"properties":{"id":"010020000A0346","commune":"01002","prefixe":"000","section":"A","numero":"346","contenance":2800,"arpente":false,"created":"2005-06-03","updated":"2018-09-25"}}
]
}
""")

df = pd.DataFrame(data['features'])
print(df)

It'll give you the following DataFrame:

      type              id                                           geometry                                         properties
0  Feature  010020000A0225  {'type': 'Polygon', 'coordinates': [[[5.430767...  {'id': '010020000A0225', 'commune': '01002', '...
1  Feature  010020000A0346  {'type': 'Polygon', 'coordinates': [[[5.424195...  {'id': '010020000A0346', 'commune': '01002', '...

From there you can easily access the geometry and properties columns.

Furthermore, if you want geometric and other properties in their own columns, you can use json_normalize :

df = pd.json_normalize(data['features'])
print(df)

Output:

      type              id geometry.type                               geometry.coordinates  ... properties.contenance properties.arpente properties.created properties.updated
0  Feature  010020000A0225       Polygon  [[[5.430767, 46.0214267], [5.4310805, 46.02201...  ...                  9440              False         2005-06-03         2018-09-25
1  Feature  010020000A0346       Polygon  [[[5.4241952, 46.0255535], [5.4233594, 46.0262...  ...                  2800              False         2005-06-03         2018-09-25