Parsing nested JSON into dataframe

Question

I am trying to parse a JSON string to its lowest granularity to a panda dataframe.

Attempts

First I tried read_json:

jsonData = pd.read_json(apiRequest)

But a large chunk of the data is still nested under networkRank.

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'])

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']])

Goal

To parse the JSON data into a flat table with all fields, which means latitude, longitude and distance data appended to each row of data in figure 2.

JSON String

{'apiVersion': '2',
 'distance': 10,
 'latitude': '-6.162959',
 'longitude': '35.751607',
 'networkRank': [{'networkId': '6402',
   'networkName': 'Vodacom',
   'type3G': {'averageRssiAsu': '9.5429091136',
    'averageRssiDb': '-69.5664329624972',
    'downloadSpeed': '1508.1304',
    'networkId': '6402',
    'networkName': 'Vodacom',
    'networkType': '3',
    'pingTime': '320.9600',
    'reliability': '0.804236452826138',
    'sampleSizeRSSI': '948',
    'sampleSizeSpeed': '29',
    'uploadSpeed': '893.7692'}},
  {'networkId': '6400',
   'networkName': 'tiGO',
   'type3G': {'averageRssiAsu': '15.3537142857',
    'averageRssiDb': '-61.4563389583101',
    'downloadSpeed': '516.0000',
    'networkId': '6400',
    'networkName': 'tiGO',
    'networkType': '3',
    'pingTime': '259.0000',
    'reliability': '0.911904765537807',
    'sampleSizeRSSI': '935',
    'sampleSizeSpeed': '21',
    'uploadSpeed': '320.4211'}},
  {'networkId': '6403',
   'networkName': 'Airtel',
   'type3G': {'averageRssiAsu': '13.2729999375',
    'averageRssiDb': '-58.1521092977699',
    'downloadSpeed': '1080.2500',
    'networkId': '6403',
    'networkName': 'Airtel',
    'networkType': '3',
    'pingTime': '194.5556',
    'reliability': '0.554680264185345',
    'sampleSizeRSSI': '587',
    'sampleSizeSpeed': '21',
    'uploadSpeed': '572.1579'}}],
 'network_type': None,
 'perMinuteCurrent': 0,
 'perMinuteLimit': 10,
 'perMonthCurrent': 0,
 'perMonthLimit': 2000}

Alexander · Accepted Answer · 2016-04-19 15:34:31Z

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

Examples:

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')])

Response:

# 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

realmaniek · Accepted Answer · 2016-04-19 14:25:50Z

0

1) Parse JSON string to python structure

2) Iterete over 'networkRank' list of dictionaries and put each key you want to add inside the hash

for data_row in deserialized_json['networkRank']:
    data_row['latitude'] = deserialized_json['latitude']
    # etc

3)

yourdataframe = pd.DataFrame( deserialized_json['networkRank'] )

answered Apr 19, 2016 at 14:25

realmaniek

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Comments

MaxU - stand with Ukraine · Accepted Answer · 2016-04-20 07:31:12Z

is that what you want?

In [22]: df = json_normalize(json_data['networkRank'])

In [23]: df['distance'] = json_data['distance']

In [24]: df['latitude'] = json_data['latitude']

In [25]: df['longitude'] = json_data['longitude']

In [26]: df
Out[26]:
  networkId networkName type3G.averageRssiAsu type3G.averageRssiDb  \
0      6402     Vodacom          9.5429091136    -69.5664329624972
1      6400        tiGO         15.3537142857    -61.4563389583101
2      6403      Airtel         13.2729999375    -58.1521092977699

  type3G.downloadSpeed type3G.networkId type3G.networkName type3G.networkType  \
0            1508.1304             6402            Vodacom                  3
1             516.0000             6400               tiGO                  3
2            1080.2500             6403             Airtel                  3

  type3G.pingTime type3G.reliability type3G.sampleSizeRSSI  \
0        320.9600  0.804236452826138                   948
1        259.0000  0.911904765537807                   935
2        194.5556  0.554680264185345                   587

  type3G.sampleSizeSpeed type3G.uploadSpeed  distance   latitude  longitude
0                     29           893.7692        10  -6.162959  35.751607
1                     21           320.4211        10  -6.162959  35.751607
2                     21           572.1579        10  -6.162959  35.751607

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