我正在解析字符串上的一些JSON数据，以将其传递给具有自定义格式的文件。 但是我需要知道哪个是文件的最后一个标记，以便我使用“}”而不是“}”（即逗号不具有正确的语法）关闭标签。
我尝试使用Last属性获取最后一个令牌，但是它不起作用。
这是我用来解析数据并写入文件的功能（它还会将所需的数据打印到终端，但不会以JSON数据打印，这就是我首先解析它的原因）：
private static void ParseData()
{
JObject jsonData;
try
{
using (StreamWriter file = new StreamWriter(Globals.dataFile))
{
file.WriteLine("{");
jsonData = JObject.Parse(Globals.json);
Console.WriteLine("*********************************************");
foreach (JToken tokens in jsonData["indicator"]["values"])
{
Console.WriteLine("Fecha y hora: " + tokens["datetime"] + " Valor: " + tokens["value"] + Environment.NewLine + "*********************************************");
file.Write(Environment.NewLine + "\t{" + Environment.NewLine + "\t\t\"datetime\": " + tokens["datetime"] + ",");
file.WriteLine("\t\t\"value\": " + tokens["value"]);
if (tokens != tokens.Last)
{
file.WriteLine("\t},");
Console.WriteLine("\t},");
}
else
{
Console.WriteLine("\t}");
}
}
file.WriteLine("}");
}
}
catch (Exception ex)
{
PrintExceptionMessage(ex);
}
}
这是我正在格式化的JSON文件的格式：
{
"indicator": {
"name": "Precio medio horario final contratación libre suma de componentes",
"short_name": "Precio medio horario final c.libre suma",
"id": 10214,
"composited": true,
"step_type": "linear",
"disaggregated": false,
"magnitud": [{
"name": "Precio",
"id": 23
}
],
"tiempo": [{
"name": "Hora",
"id": 4
}
],
"geos": [{
"geo_id": 8741,
"geo_name": "Península"
}
],
"values_updated_at": "2019-01-31T08:05:32.000+01:00",
"values": [{
"value": 49.38,
"datetime": "2019-01-31T01:00:00.000+01:00",
"datetime_utc": "2019-01-31T00:00:00Z",
"tz_time": "2019-01-31T00:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 47.46,
"datetime": "2019-01-31T02:00:00.000+01:00",
"datetime_utc": "2019-01-31T01:00:00Z",
"tz_time": "2019-01-31T01:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 46.82,
"datetime": "2019-01-31T03:00:00.000+01:00",
"datetime_utc": "2019-01-31T02:00:00Z",
"tz_time": "2019-01-31T02:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 46.5,
"datetime": "2019-01-31T04:00:00.000+01:00",
"datetime_utc": "2019-01-31T03:00:00Z",
"tz_time": "2019-01-31T03:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 47.36,
"datetime": "2019-01-31T05:00:00.000+01:00",
"datetime_utc": "2019-01-31T04:00:00Z",
"tz_time": "2019-01-31T04:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 50.05,
"datetime": "2019-01-31T06:00:00.000+01:00",
"datetime_utc": "2019-01-31T05:00:00Z",
"tz_time": "2019-01-31T05:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 56.61,
"datetime": "2019-01-31T07:00:00.000+01:00",
"datetime_utc": "2019-01-31T06:00:00Z",
"tz_time": "2019-01-31T06:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 65.58,
"datetime": "2019-01-31T08:00:00.000+01:00",
"datetime_utc": "2019-01-31T07:00:00Z",
"tz_time": "2019-01-31T07:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 66.59,
"datetime": "2019-01-31T09:00:00.000+01:00",
"datetime_utc": "2019-01-31T08:00:00Z",
"tz_time": "2019-01-31T08:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 66.49,
"datetime": "2019-01-31T10:00:00.000+01:00",
"datetime_utc": "2019-01-31T09:00:00Z",
"tz_time": "2019-01-31T09:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 65.54,
"datetime": "2019-01-31T11:00:00.000+01:00",
"datetime_utc": "2019-01-31T10:00:00Z",
"tz_time": "2019-01-31T10:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 63.71,
"datetime": "2019-01-31T12:00:00.000+01:00",
"datetime_utc": "2019-01-31T11:00:00Z",
"tz_time": "2019-01-31T11:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 61.94,
"datetime": "2019-01-31T13:00:00.000+01:00",
"datetime_utc": "2019-01-31T12:00:00Z",
"tz_time": "2019-01-31T12:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 56.58,
"datetime": "2019-01-31T14:00:00.000+01:00",
"datetime_utc": "2019-01-31T13:00:00Z",
"tz_time": "2019-01-31T13:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}, {
"value": 56.08,
"datetime": "2019-01-31T15:00:00.000+01:00",
"datetime_utc": "2019-01-31T14:00:00Z",
"tz_time": "2019-01-31T14:00:00.000Z",
"geo_id": 8741,
"geo_name": "Península"
}
]
}
}
我仅从此处获取“值”和“日期时间”数据。
我现在获取的文件具有这种格式（真的很不好，很抱歉）：
{
{
"datetime": 01/02/2019 01:00:00, "value": 48,68
},
{
"datetime": 01/02/2019 02:00:00, "value": 45,7
},
{
"datetime": 01/02/2019 03:00:00, "value": 44,1
},
{
"datetime": 01/02/2019 04:00:00, "value": 44,53
},
{
"datetime": 01/02/2019 05:00:00, "value": 46,87
},
{
"datetime": 01/02/2019 06:00:00, "value": 49,84
},
{
"datetime": 01/02/2019 07:00:00, "value": 56,69
},
{
"datetime": 01/02/2019 08:00:00, "value": 62,5
},
}
我想得到的是：
{
{
"datetime": 01/02/2019 01:00:00,
"value": 48,68
},{
"datetime": 01/02/2019 02:00:00,
"value": 45,7
},{
"datetime": 01/02/2019 03:00:00,
"value": 44,1
},{
"datetime": 01/02/2019 04:00:00,
"value": 44,53
},{
"datetime": 01/02/2019 05:00:00,
"value": 46,87
},{
"datetime": 01/02/2019 06:00:00,
"value": 49,84
},{
"datetime": 01/02/2019 07:00:00,
"value": 56,69
},{
"datetime": 01/02/2019 08:00:00,
"value": 62,5
}
}
非常感谢您，对不起。我真的是JSON的新手，并在C＃上对其进行格式化。
答案 0 :(得分：0)
您需要将json中选择的键/值对解析为类对象列表，然后将其再次保存到新文件中，例如
private static void ParseData()
{
string json = File.ReadAllText(@"Path to your input json file");
JObject jsonData = JObject.Parse(json);
List<MyCustomClass> output = new List<MyCustomClass>();
foreach (JToken tokens in jsonData["indicator"]["values"])
{
output.Add(new MyCustomClass { datetime = tokens["datetime"].ToString(), value = tokens["value"].ToString() });
}
File.WriteAllText(@"Path to your output json file", JToken.FromObject(output).ToString());
}
并且您需要一个类对象来解析键/值对，
class MyCustomClass
{
public string datetime { get; set; }
public string value { get; set; }
}
输出：
[
{
"datetime": "31-01-2019 05:30:00 AM",
"value": "49.38"
},
{
"datetime": "31-01-2019 06:30:00 AM",
"value": "47.46"
},
{
"datetime": "31-01-2019 07:30:00 AM",
"value": "46.82"
},
{
"datetime": "31-01-2019 08:30:00 AM",
"value": "46.5"
},
{
"datetime": "31-01-2019 09:30:00 AM",
"value": "47.36"
},
{
"datetime": "31-01-2019 10:30:00 AM",
"value": "50.05"
},
{
"datetime": "31-01-2019 11:30:00 AM",
"value": "56.61"
},
{
"datetime": "31-01-2019 12:30:00 PM",
"value": "65.58"
},
{
"datetime": "31-01-2019 01:30:00 PM",
"value": "66.59"
},
{
"datetime": "31-01-2019 02:30:00 PM",
"value": "66.49"
},
{
"datetime": "31-01-2019 03:30:00 PM",
"value": "65.54"
},
{
"datetime": "31-01-2019 04:30:00 PM",
"value": "63.71"
},
{
"datetime": "31-01-2019 05:30:00 PM",
"value": "61.94"
},
{
"datetime": "31-01-2019 06:30:00 PM",
"value": "56.58"
},
{
"datetime": "31-01-2019 07:30:00 PM",
"value": "56.08"
}
]