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python - 我如何解析具有相同多个键的json：python中的值对，稍后我需要求和

问题描述

我需要解析与下面的示例相同的 JSON 文件。我需要将每个属性值与其他属性值相加，以将每个人都聚集在一起，就像p_30_34_yrs与其他属性一样p_30_34_yrs等等。我需要 JSON 文件作为带有这些附加值的结果p_30_34_yrs- 一起，p_tot 一起等等。请帮助我，因为我对 Python 很陌生。我能够解析它load()并给我dict，但是当我喜欢dict["features"]它时，它会将它转换为list甚至在list我无法获取我应用的每个属性键值sum()。

Example:
{
"type":"..",
"box":[..],
"crs":{
..
},
"features":[
    {
      "type": "Feature",
      "geometry": {
        "type": "MultiPolygon",
        "coordinates": [..]
},
      "properties": {
        "p_30_34_yrs": 421,
        "p_tot": 3210,
        "year": "2014",
        "p_75_79_yrs": 30,
        "p_40_44_yrs": 259,
        "p_55_59_yrs": 174,
        "p_65_69_yrs": 96
      },
      "id": "lga_nrp_people_2010_2014.fid--2ea3ac96_1631a637772_-4cb1"
    },
{
      "type": "Feature",
      "geometry": {
        "type": "MultiPolygon",
        "coordinates": [
          ..
     ]
      },
      "properties": {
         "p_30_34_yrs": 2316,
         "p_tot": 22690,
         "year": "2014",
         "p_75_79_yrs": 461,
         "p_40_44_yrs": 1211,
         "p_55_59_yrs": 1031,
         "p_65_69_yrs": 1071
       },
       "id": "lga_nrp_people_2010_2014.fid--2ea3ac96_1631a637772_-4cb0"
    },
and same way 6 more times.
]
}

标签： pythonjson

解决方案

像这样的东西（测试代码）？

jsn = {
    "type": "..",
    "box": "[..]",
    "features": [{
            "type": "Feature",
            "geometry": {
                "type": "MultiPolygon",
                "coordinates": "[..]"
            },
            "properties": {
                "p_30_34_yrs": 421,
                "p_tot": 3210,
                "year": "2014",
                "p_75_79_yrs": 30,
                "p_40_44_yrs": 259,
                "p_55_59_yrs": 174,
                "p_65_69_yrs": 96
            },
            "id": "lga_nrp_people_2010_2014.fid--2ea3ac96_1631a637772_-4cb1"
        },
        {
            "type": "Feature",
            "geometry": {
                "type": "MultiPolygon"
            },
            "properties": {
                "p_30_34_yrs": 2316,
                "p_tot": 22690,
                "year": "2014",
                "p_75_79_yrs": 461,
                "p_40_44_yrs": 1211,
                "p_55_59_yrs": 1031,
                "p_65_69_yrs": 1071
            },
            "id": "lga_nrp_people_2010_2014.fid--2ea3ac96_1631a637772_-4cb0"
        }
    ]
}


data = jsn["features"]

props = {"p_30_34_yrs": 0,
                "p_tot": 0,
                "p_75_79_yrs": 0,
                "p_40_44_yrs": 0,
                "p_55_59_yrs": 0,
                "p_65_69_yrs": 0}

for feat in data:
    if "properties" in feat:
        if "p_30_34_yrs" in feat["properties"]: props["p_30_34_yrs"] += feat["properties"]["p_30_34_yrs"]
        if "p_tot" in feat["properties"]: props["p_tot"] += feat["properties"]["p_tot"]
        if "p_75_79_yrs" in feat["properties"]: props["p_75_79_yrs"] += feat["properties"]["p_75_79_yrs"]
        if "p_40_44_yrs" in feat["properties"]: props["p_40_44_yrs"] += feat["properties"]["p_40_44_yrs"]
        if "p_55_59_yrs" in feat["properties"]: props["p_55_59_yrs"] += feat["properties"]["p_55_59_yrs"]
        if "p_65_69_yrs" in feat["properties"]: props["p_65_69_yrs"] += feat["properties"]["p_65_69_yrs"]

print(props)

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