将嵌套的JSON解析为数据框 [英] Parsing nested JSON into dataframe
问题描述
我正在尝试将JSON字符串的最低粒度解析为熊猫数据帧.
I am trying to parse a JSON string to its lowest granularity to a panda dataframe.
首先,我尝试了read_json:
First I tried read_json:
jsonData = pd.read_json(apiRequest)
但是大部分数据仍嵌套在networkRank下.
But a large chunk of the data is still nested under networkRank.
然后我尝试了json_normalize,但是这次我错过了更高一级的数据,例如纬度和经度.
Then I tried json_normalize, but this time I am missing the data one level higher such as latitude and longitude.
result = json_normalize(json_data['networkRank'])
我还尝试将解析"为嵌套结构并从头开始构建数据帧,但是此代码会导致错误:
I also tried to parse "into" the nested structure and construct the data frame from scratch, but this code results in error:
result_nested = json_normalize(json_data, 'networkRank', ['longitude', 'latitude', ['networkRank', 'type3G', 'downloadSpeed']])
目标
要将JSON数据解析为具有所有字段的平面表,这意味着将纬度,经度和距离数据附加到图
推荐答案
此函数以递归方式调用自身以拼合字典和列表.
This function recursively calls itself to flatten dictionaries and lists.
from collections import OrderedDict
def flatten(json_object, container=None, name=''):
if container is None:
container = OrderedDict()
if isinstance(json_object, dict):
for key in json_object:
flatten(json_object[key], container=container, name=name + key + '_')
elif isinstance(json_object, list):
for n, item in enumerate(json_object, 1):
flatten(item, container=container, name=name + str(n) + '_')
else:
container[str(name[:-1])] = str(json_object)
return container
示例:
flatten([1, 2, 3])
OrderedDict([('1', '1'), ('2', '2'), ('3', '3')])
flatten([1, 2, 3], name='x')
OrderedDict([('x1', '1'), ('x2', '2'), ('x3', '3')])
flatten({'a': [1, 2, 3], 'b': 4, 'c': {'d': [5, 6], 'e': 7}}, name='x')
OrderedDict([('xa_1', '1'),
('xa_2', '2'),
('xa_3', '3'),
('xc_e', '7'),
('xc_d_1', '5'),
('xc_d_2', '6'),
('xb', '4')])
响应:
# j = json string
>>> pd.DataFrame(flatten(j), index=[0]).T
0
perMinuteLimit 10
distance 10
perMonthCurrent 0
longitude 35.751607
perMonthLimit 2000
latitude -6.162959
perMinuteCurrent 0
networkRank_1_networkId 6402
networkRank_1_type3G_sampleSizeSpeed 29
networkRank_1_type3G_averageRssiAsu 9.5429091136
networkRank_1_type3G_pingTime 320.9600
networkRank_1_type3G_networkType 3
networkRank_1_type3G_averageRssiDb -69.5664329624972
networkRank_1_type3G_networkName Vodacom
networkRank_1_type3G_networkId 6402
networkRank_1_type3G_downloadSpeed 1508.1304
networkRank_1_type3G_uploadSpeed 893.7692
networkRank_1_type3G_reliability 0.804236452826138
networkRank_1_type3G_sampleSizeRSSI 948
networkRank_1_networkName Vodacom
networkRank_2_networkId 6400
networkRank_2_type3G_sampleSizeSpeed 21
networkRank_2_type3G_averageRssiAsu 15.3537142857
networkRank_2_type3G_pingTime 259.0000
networkRank_2_type3G_networkType 3
networkRank_2_type3G_averageRssiDb -61.4563389583101
networkRank_2_type3G_networkName tiGO
networkRank_2_type3G_networkId 6400
networkRank_2_type3G_downloadSpeed 516.0000
networkRank_2_type3G_uploadSpeed 320.4211
networkRank_2_type3G_reliability 0.911904765537807
networkRank_2_type3G_sampleSizeRSSI 935
networkRank_2_networkName tiGO
networkRank_3_networkId 6403
networkRank_3_type3G_sampleSizeSpeed 21
networkRank_3_type3G_averageRssiAsu 13.2729999375
networkRank_3_type3G_pingTime 194.5556
networkRank_3_type3G_networkType 3
networkRank_3_type3G_averageRssiDb -58.1521092977699
networkRank_3_type3G_networkName Airtel
networkRank_3_type3G_networkId 6403
networkRank_3_type3G_downloadSpeed 1080.2500
networkRank_3_type3G_uploadSpeed 572.1579
networkRank_3_type3G_reliability 0.554680264185345
networkRank_3_type3G_sampleSizeRSSI 587
networkRank_3_networkName Airtel
network_type None
apiVersion 2
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