如何基于时间戳将json转换为dataframe

Question

我有这样一个数据：

[
    {
        "timestamp": 1572430625231,
        "feature": "brakePressure",
        "value": 0
    },
    {
        "timestamp": 1572430625231,
        "feature": "lateralAcceleration",
        "value": 0
    },
    {
        "timestamp": 1572430625231,
        "feature": "longitudinalAcceleration",
        "value": 0
    },
    {
        "timestamp": 1572430625231,
        "feature": "acceleratorPosition",
        "value": 0
    },
    {
        "timestamp": 1572430625231,
        "feature": "currentTorque",
        "value": 0
    },
    {
        "timestamp": 1572430625231,
        "feature": "currentOutputPower",
        "value": 0
    },
    {
        "timestamp": 1572430625232,
        "feature": "yawRate",
        "value": 0
    },
    {
        "timestamp": 1572430625232,
        "feature": "wheelAngle",
        "value": 10
    },
    {
        "timestamp": 1572430625232,
        "feature": "vehicleSpeed/speed",
        "value": 0
    },
    {
        "timestamp": 1572430625232,
        "feature": "vehicleSpeed/state",
        "value": 0
    },
    {
        "timestamp": 1572430625232,
        "feature": "vehicleSpeed/unit",
        "value": 0
    },
    {
        "timestamp": 1572430625232,
        "feature": "relAllWheelDriveTorque/relAllWheelClutchTorque",
        "value": 0.24
    },
    {
        "timestamp": 1572430625246,
        "feature": "navPosition_HP/longitude",
        "value": 8.899101
    },
    {
        "timestamp": 1572430625246,
        "feature": "navPosition_HP/velocity",
        "value": 0
    },
    {
        "timestamp": 1572430625246,
        "feature": "navPosition_HP/latitude",
        "value": 48.850462
    },
    {
        "timestamp": 1572430625246,
        "feature": "navPosition_HP/horizStdDeviation",
        "value": 60
    },
    {
        "timestamp": 1572430625246,
        "feature": "navPosition_HP/heading",
        "value": 354
    },
    {
        "timestamp": 1572430625246,
        "feature": "navPosition_HP/altitude",
        "value": 456
    },
    {
        "timestamp": 1572430625445,
        "feature": "navPosition_HP/longitude",
        "value": 8.899101
    },
    {
        "timestamp": 1572430625445,
        "feature": "navPosition_HP/velocity",
        "value": 0
    },
    {
        "timestamp": 1572430625445,
        "feature": "navPosition_HP/latitude",
        "value": 48.850462
    },
    {
        "timestamp": 1572430625445,
        "feature": "navPosition_HP/horizStdDeviation",
        "value": 60
    },
    {
        "timestamp": 1572430625445,
        "feature": "navPosition_HP/heading",
        "value": 354
    },
    {
        "timestamp": 1572430625445,
        "feature": "navPosition_HP/altitude",
        "value": 456
    },
    {
        "timestamp": 1572430625647,
        "feature": "navPosition_HP/longitude",
        "value": 8.899101
    },
    {
        "timestamp": 1572430625647,
        "feature": "navPosition_HP/velocity",
        "value": 0
    },
    {
        "timestamp": 1572430625647,
        "feature": "navPosition_HP/latitude",
        "value": 48.850462
    },
    {
        "timestamp": 1572430625647,
        "feature": "navPosition_HP/horizStdDeviation",
        "value": 60
    },
    {
        "timestamp": 1572430625647,
        "feature": "navPosition_HP/heading",
        "value": 354
    },
    {
        "timestamp": 1572430625647,
        "feature": "navPosition_HP/altitude",
        "value": 456
    },
    {
        "timestamp": 1572430625893,
        "feature": "navPosition_HP/longitude",
        "value": 8.899101
    },
    {
        "timestamp": 1572430625893,
        "feature": "navPosition_HP/velocity",
        "value": 0
    },
    {
        "timestamp": 1572430625893,
        "feature": "navPosition_HP/latitude",
        "value": 48.850462
    },
    {
        "timestamp": 1572430625893,
        "feature": "navPosition_HP/horizStdDeviation",
        "value": 60
    },
    {
        "timestamp": 1572430625893,
        "feature": "navPosition_HP/heading",
        "value": 354
    },
    {
        "timestamp": 1572430625893,
        "feature": "navPosition_HP/altitude",
        "value": 456
    },
    {
        "timestamp": 1572430626046,
        "feature": "navPosition_HP/longitude",
        "value": 8.899101
    },
    {
        "timestamp": 1572430626046,
        "feature": "navPosition_HP/velocity",
        "value": 0
    },
    {
        "timestamp": 1572430626046,
        "feature": "navPosition_HP/latitude",
        "value": 48.850462
    },
    {
        "timestamp": 1572430626046,
        "feature": "navPosition_HP/horizStdDeviation",
        "value": 60
    },
    {
        "timestamp": 1572430626046,
        "feature": "navPosition_HP/heading",
        "value": 354
    },
    {
        "timestamp": 1572430626046,
        "feature": "navPosition_HP/altitude",
        "value": 456
    },
    {
        "timestamp": 1572430626247,
        "feature": "navPosition_HP/longitude",
        "value": 8.899101
    },
    {
        "timestamp": 1572430626247,
        "feature": "navPosition_HP/velocity",
        "value": 0
    },
    {
        "timestamp": 1572430626247,
        "feature": "navPosition_HP/latitude",
        "value": 48.850462
    },
    {
        "timestamp": 1572430626247,
        "feature": "navPosition_HP/horizStdDeviation",
        "value": 60
    },
    {
        "timestamp": 1572430626247,
        "feature": "navPosition_HP/heading",
        "value": 354
    },
    {
        "timestamp": 1572430626247,
        "feature": "navPosition_HP/altitude",
        "value": 456
    },
    {
        "timestamp": 1572430626445,
        "feature": "navPosition_HP/longitude",
        "value": 8.899101
    },
    {
        "timestamp": 1572430626445,
        "feature": "navPosition_HP/velocity",
        "value": 0
    },
    {
        "timestamp": 1572430626445,
        "feature": "navPosition_HP/latitude",
        "value": 48.850462
    },
    {
        "timestamp": 1572430626445,
        "feature": "navPosition_HP/horizStdDeviation",
        "value": 60
    },
    {
        "timestamp": 1572430626445,
        "feature": "navPosition_HP/heading",
        "value": 354
    },
    {
        "timestamp": 1572430626445,
        "feature": "navPosition_HP/altitude",
        "value": 456
    },
    {
        "timestamp": 1572430626533,
        "feature": "acceleratorPosition",
        "value": 0
    },
    {
        "timestamp": 1572430626533,
        "feature": "brakePressure",
        "value": 0
    },
    {
        "timestamp": 1572430626533,
        "feature": "currentOutputPower",
        "value": 0
    },
    {
        "timestamp": 1572430626533,
        "feature": "currentTorque",
        "value": 0
    },....]

我想根据时间戳将这个json数据转换成一个dataframe。这很复杂，但我会尽力解释的。我希望得到每个时间戳中的经度和纬度，并希望100毫秒内的其他特性位于数据帧中的同一行。 这些是我想到的步骤：

1. 将纬度和经度特征的所有时间戳分组
2. 向前看100毫秒，向后看100毫秒 查找该范围内的其他功能
3. 将该范围（100毫秒）内的功能放在同一个位置 经纬度及其时间戳所在的行。你知道吗
4. 如果找到两个同名的功能，则采用该功能 并把它放在同一行经度上 和纬度
5. 如果找不到特征，则放置NaN
6. 将数据按经度和时间戳分组后 纬度，现在我可以用pandas.readïjson或 pandas.DataFrame.from目录你知道吗

我试着这样做，但我总是得到一个奇怪的行为，它也是非常缓慢。也许有人能教我怎么做。我会感激任何帮助，我愿意使用任何图书馆在那里。我现在使用熊猫，但我不知道是否有任何有用的方法，我的任务存在。我也在使用python3.7