在熊猫数据框中展平嵌套的Json

在熊猫数据框中展平嵌套的Json

如果您正在寻找一种更通用的方法来从json展开多个层次结构，则可以使用

recursion

并列出理解来重塑数据。下面介绍了一种替代方法：

def flatten_json(nested_json, exclude=['']):    """Flatten json object with nested keys into a single level.        Args: nested_json: A nested json object. exclude: Keys to exclude from output.        Returns: The flattened json object if successful, None otherwise.    """    out = {}    def flatten(x, name='', exclude=exclude):        if type(x) is dict: for a in x:     if a not in exclude: flatten(x[a], name + a + '_')        elif type(x) is list: i = 0 for a in x:     flatten(a, name + str(i) + '_')     i += 1        else: out[name[:-1]] = x    flatten(nested_json)    return out

然后，您可以独立于嵌套级别将其应用于数据：

新样本数据

this_dict = {'events': [  {'id': 142896214,   'playerId': 37831,   'teamId': 3157,   'matchId': 2214569,   'matchPeriod': '1H',   'eventSec': 0.8935539999999946,   'eventId': 8,   'eventName': 'Pass',   'subEventId': 85,   'subEventName': 'Simple pass',   'positions': [{'x': 51, 'y': 49}, {'x': 40, 'y': 53}],   'tags': [{'id': 1801, 'tag': {'label': 'accurate'}}]}, {'id': 142896214,   'playerId': 37831,   'teamId': 3157,   'matchId': 2214569,   'matchPeriod': '1H',   'eventSec': 0.8935539999999946,   'eventId': 8,   'eventName': 'Pass',   'subEventId': 85,   'subEventName': 'Simple pass',   'positions': [{'x': 51, 'y': 49}, {'x': 40, 'y': 53},{'x': 51, 'y': 49}],   'tags': [{'id': 1801, 'tag': {'label': 'accurate'}}]}]}

用法

pd.Dataframe([flatten_json(x) for x in this_dict['events']])Out[1]:          id  playerId  teamId  matchId matchPeriod  eventSec  eventId    142896214     37831    3157  2214569          1H  0.893554        8   1  142896214     37831    3157  2214569          1H  0.893554        8  eventName  subEventId subEventName  positions_0_x  positions_0_y        Pass          85  Simple pass  51  49   1      Pass          85  Simple pass  51  49   positions_1_x  positions_1_y  tags_0_id tags_0_tag_label  positions_2_x    40  53       1801         accurate NaN   1  40  53       1801         accurate51.0   positions_2_y  0 NaN  149.0

请注意，该

flatten_json

代码不是我的代码，我在这里和这里都看到了它，而对原始源代码的不确定性很高。