我有一个Json字符串，我可以从中提取一些组件，例如params[:movies][idx]
，formatted_address，lat，但我无法提取功能（其他组成部分，如<{> 1}}下的 intersection，political，country，administrative_area_level_1，administrative_area_level_2，administrative_area_level_3，administrative_area_level_4，administrative_area_level_5，colloquial_area，locality，ward，neighborhood，premise，subpremise等
我期待像
lng以下是示例JSON字符串
long_name以下是代码片段
formatted_address px_val py_val political country administrative_area_level_1 .. ..
Satya Niwas, Kanti Nagar.. 19.1096591 72.8674712 Kanti Nagar,JB Nagar India maharashtra .. ..
82, Bamanpuri, Ajit Nagar.. 19.109749 72.867249 Bamanpuri India maharashtra .. ..
.
.
.
它给出了错误
{'results': [{'address_components': [{'long_name': 'Satya Niwas',
'short_name': 'Satya Niwas',
'types': ['establishment', 'point_of_interest', 'premise']},
{'long_name': 'Kanti Nagar',
'short_name': 'Kanti Nagar',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400059', 'short_name': '400059', 'types': ['postal_code']}],
'formatted_address': 'Satya Niwas, Kanti Nagar, J B Nagar, Andheri East, Mumbai, Maharashtra 400059, India',
'geometry': {'bounds': {'northeast': {'lat': 19.1097923, 'lng': 72.8675306},
'southwest': {'lat': 19.1095784, 'lng': 72.8673391}},
'location': {'lat': 19.1096591, 'lng': 72.8674712},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 19.1110343302915,
'lng': 72.8687838302915},
'southwest': {'lat': 19.1083363697085, 'lng': 72.86608586970848}}},
'place_id': 'ChIJ4UsP5DjI5zsR8hgwhHo9wEk',
'types': ['establishment', 'point_of_interest', 'premise']},
{'address_components': [{'long_name': '82',
'short_name': '82',
'types': ['premise']},
{'long_name': 'Bamanpuri',
'short_name': 'Bamanpuri',
'types': ['neighborhood', 'political']},
{'long_name': 'Ajit Nagar',
'short_name': 'Ajit Nagar',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400053', 'short_name': '400053', 'types': ['postal_code']}],
'formatted_address': '82, Bamanpuri, Ajit Nagar, J B Nagar, Andheri East, Mumbai, Maharashtra 400053, India',
'geometry': {'location': {'lat': 19.109749, 'lng': 72.867249},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 19.1110979802915,
'lng': 72.8685979802915},
'southwest': {'lat': 19.1084000197085, 'lng': 72.86590001970849}}},
'place_id': 'ChIJqYip4zjI5zsR0Yg8bdXQX3o',
'types': ['street_address']},
{'address_components': [{'long_name': 'Todi Building',
'short_name': 'Todi Building',
'types': ['premise']},
{'long_name': 'Sheth Bhavanidas Benani Marg',
'short_name': 'Sheth Bhavanidas Benani Marg',
'types': ['route']},
{'long_name': 'Kanti Nagar',
'short_name': 'Kanti Nagar',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400059', 'short_name': '400059', 'types': ['postal_code']}],
'formatted_address': 'Todi Building, Sheth Bhavanidas Benani Marg, Kanti Nagar, J B Nagar, Andheri East, Mumbai, Maharashtra 400059, India',
'geometry': {'location': {'lat': 19.1098265, 'lng': 72.86778869999999},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 19.1111754802915,
'lng': 72.86913768029149},
'southwest': {'lat': 19.1084775197085, 'lng': 72.86643971970848}}},
'place_id': 'ChIJo5bq3zjI5zsR2hRaNQF3xd0',
'types': ['premise']},
{'address_components': [{'long_name': 'KASI APARTMENTS',
'short_name': 'KASI APARTMENTS',
'types': ['establishment', 'point_of_interest', 'premise']},
{'long_name': 'Shriniwas Bagarka Road',
'short_name': 'Shriniwas Bagarka Rd',
'types': ['route']},
{'long_name': 'Bamanpuri',
'short_name': 'Bamanpuri',
'types': ['neighborhood', 'political']},
{'long_name': 'Kanti Nagar',
'short_name': 'Kanti Nagar',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400059', 'short_name': '400059', 'types': ['postal_code']}],
'formatted_address': 'KASI APARTMENTS, Shriniwas Bagarka Rd, Bamanpuri, Kanti Nagar, J B Nagar, Andheri East, Mumbai, Maharashtra 400059, India',
'geometry': {'location': {'lat': 19.1093338, 'lng': 72.8670515},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 19.1106827802915,
'lng': 72.86840048029151},
'southwest': {'lat': 19.10798481970849, 'lng': 72.86570251970849}}},
'place_id': 'ChIJoUz25DjI5zsRiMoiQtq5kXs',
'types': ['establishment', 'point_of_interest', 'premise']},
{'address_components': [{'long_name': 'Silver Line Apts.',
'short_name': 'Silver Line Apts.',
'types': ['premise']},
{'long_name': 'Bamanpuri',
'short_name': 'Bamanpuri',
'types': ['neighborhood', 'political']},
{'long_name': 'J.B. Nagar',
'short_name': 'J.B. Nagar',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400047', 'short_name': '400047', 'types': ['postal_code']}],
'formatted_address': 'Silver Line Apts., Bamanpuri, J.B. Nagar, J B Nagar, Andheri East, Mumbai, Maharashtra 400047, India',
'geometry': {'location': {'lat': 19.1091075, 'lng': 72.8670776},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 19.1104564802915,
'lng': 72.86842658029151},
'southwest': {'lat': 19.1077585197085, 'lng': 72.86572861970849}}},
'place_id': 'ChIJEQ3_ZzjI5zsR9LxIP1h2b2c',
'types': ['premise']},
{'address_components': [{'long_name': 'Gokul panch chs',
'short_name': 'Gokul panch chs',
'types': ['establishment', 'point_of_interest']},
{'long_name': '81-B', 'short_name': '81-B', 'types': ['street_number']},
{'long_name': 'Sheth Bhavanidas Benani Marg',
'short_name': 'Sheth Bhavanidas Benani Marg',
'types': ['route']},
{'long_name': 'Bamanpuri',
'short_name': 'Bamanpuri',
'types': ['neighborhood', 'political']},
{'long_name': 'Ajit Nagar',
'short_name': 'Ajit Nagar',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400047', 'short_name': '400047', 'types': ['postal_code']}],
'formatted_address': 'Gokul panch chs, 81-B, 81-B, Sheth Bhavanidas Benani Marg, Bamanpuri, Ajit Nagar, J B Nagar, Andheri East, Mumbai, Maharashtra 400047, India',
'geometry': {'location': {'lat': 19.1098713, 'lng': 72.86705669999999},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 19.1112202802915,
'lng': 72.8684056802915},
'southwest': {'lat': 19.1085223197085, 'lng': 72.8657077197085}}},
'place_id': 'ChIJpUuz4jjI5zsRpgQdmR5E1v0',
'types': ['establishment', 'point_of_interest']},
{'address_components': [{'long_name': 'Ajit Nagar',
'short_name': 'Ajit Nagar',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400047', 'short_name': '400047', 'types': ['postal_code']}],
'formatted_address': 'Ajit Nagar, J B Nagar, Andheri East, Mumbai, Maharashtra 400047, India',
'geometry': {'bounds': {'northeast': {'lat': 19.1119198, 'lng': 72.8714133},
'southwest': {'lat': 19.1085396, 'lng': 72.8662167}},
'location': {'lat': 19.1103164, 'lng': 72.8680732},
'location_type': 'APPROXIMATE',
'viewport': {'northeast': {'lat': 19.1119198, 'lng': 72.8714133},
'southwest': {'lat': 19.1085396, 'lng': 72.8662167}}},
'place_id': 'ChIJPWPg4zjI5zsRJWPFphEkcxc',
'types': ['political', 'sublocality', 'sublocality_level_3']},
{'address_components': [{'long_name': 'Bamanpuri',
'short_name': 'Bamanpuri',
'types': ['neighborhood', 'political']},
{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400047', 'short_name': '400047', 'types': ['postal_code']}],
'formatted_address': 'Bamanpuri, J B Nagar, Andheri East, Mumbai, Maharashtra 400047, India',
'geometry': {'bounds': {'northeast': {'lat': 19.1102874, 'lng': 72.869838},
'southwest': {'lat': 19.1060651, 'lng': 72.8635609}},
'location': {'lat': 19.1084347, 'lng': 72.86574929999999},
'location_type': 'APPROXIMATE',
'viewport': {'northeast': {'lat': 19.1102874, 'lng': 72.869838},
'southwest': {'lat': 19.1060651, 'lng': 72.8635609}}},
'place_id': 'ChIJIYgnUDjI5zsRK_Zl9Zy_QkY',
'types': ['neighborhood', 'political']},
{'address_components': [{'long_name': 'J B Nagar',
'short_name': 'J B Nagar',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']},
{'long_name': '400047', 'short_name': '400047', 'types': ['postal_code']}],
'formatted_address': 'J B Nagar, Andheri East, Mumbai, Maharashtra 400047, India',
'geometry': {'bounds': {'northeast': {'lat': 19.1161579, 'lng': 72.871533},
'southwest': {'lat': 19.1008041, 'lng': 72.8606231}},
'location': {'lat': 19.1110621, 'lng': 72.8655922},
'location_type': 'APPROXIMATE',
'viewport': {'northeast': {'lat': 19.1161579, 'lng': 72.871533},
'southwest': {'lat': 19.1008041, 'lng': 72.8606231}}},
'place_id': 'ChIJt8_u6TjI5zsRR9eE5rMK45A',
'types': ['political', 'sublocality', 'sublocality_level_2']},
{'address_components': [{'long_name': 'Andheri East',
'short_name': 'Andheri East',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': 'Mumbai',
'short_name': 'Mumbai',
'types': ['locality', 'political']},
{'long_name': 'Mumbai Suburban',
'short_name': 'Mumbai Suburban',
'types': ['administrative_area_level_2', 'political']},
{'long_name': 'Maharashtra',
'short_name': 'MH',
'types': ['administrative_area_level_1', 'political']},
{'long_name': 'India',
'short_name': 'IN',
'types': ['country', 'political']}],
'formatted_address': 'Andheri East, Mumbai, Maharashtra, India',
'geometry': {'bounds': {'northeast': {'lat': 19.1327276,
'lng': 72.89305499999999},
'southwest': {'lat': 19.096748, 'lng': 72.843926}},
'location': {'lat': 19.1154908, 'lng': 72.8726952},
'location_type': 'APPROXIMATE',
'viewport': {'northeast': {'lat': 19.1327276, 'lng': 72.89305499999999},
'southwest': {'lat': 19.096748, 'lng': 72.843926}}},
'place_id': 'ChIJMbHfQRu25zsRMazdY3UpaKY',
'types': ['political', 'sublocality', 'sublocality_level_1']}],
'status': 'OK'}
我获得了预期的O / P的前3列但无法提取其他列。 任何有关相同的建议都会有所帮助
由于
DOMNICK
答案 0 :(得分：2)
我会选择json_normalize，想到一行答案，但我认为不可能，（这里我只为px_val和py_val做了，你可以为其他列做类似的事情）
from pandas.io.json import json_normalize
import pandas as pd
import json
with open('dat.json') as f:
data = json.load(f)
result = json_normalize(data,'results')
result['px_val'] = result['geometry'].apply(json_normalize).apply(lambda x : x['location.lat'])
result['py_val'] = result['geometry'].apply(json_normalize).apply(lambda x : x['location.lng'])
print(result[['formatted_address','px_val','py_val']])
formatted_address px_val py_val 0 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471 1 82, Bamanpuri, Ajit Nagar, J B Nagar, Andheri ... 19.109749 72.867249 2 Todi Building, Sheth Bhavanidas Benani Marg, K... 19.109827 72.867789 3 KASI APARTMENTS, Shriniwas Bagarka Rd, Bamanpu... 19.109334 72.867052 4 Silver Line Apts., Bamanpuri, J.B. Nagar, J B ... 19.109108 72.867078 5 Gokul panch chs, 81-B, 81-B, Sheth Bhavanidas ... 19.109871 72.867057 6 Ajit Nagar, J B Nagar, Andheri East, Mumbai, M... 19.110316 72.868073 7 Bamanpuri, J B Nagar, Andheri East, Mumbai, Ma... 19.108435 72.865749 8 J B Nagar, Andheri East, Mumbai, Maharashtra 4... 19.111062 72.865592 9 Andheri East, Mumbai, Maharashtra, India 19.115491 72.872695
我试图解析政治当然不为这个解决方案感到自豪，即
pol = []
for i in result['address_components'].apply(json_normalize):
pol.append(','.join(i.apply(lambda x : x['long_name'] if 'political' in x['types'] else np.nan,1).dropna()))
result['political'] = pol
输出result['political']
0 Kanti Nagar,J B Nagar,Andheri East,Mumbai,Mumb... 1 Bamanpuri,Ajit Nagar,J B Nagar,Andheri East,Mu... 2 Kanti Nagar,J B Nagar,Andheri East,Mumbai,Mumb... 3 Bamanpuri,Kanti Nagar,J B Nagar,Andheri East,M... 4 Bamanpuri,J.B. Nagar,J B Nagar,Andheri East,Mu... 5 Bamanpuri,Ajit Nagar,J B Nagar,Andheri East,Mu... 6 Ajit Nagar,J B Nagar,Andheri East,Mumbai,Mumba... 7 Bamanpuri,J B Nagar,Andheri East,Mumbai,Mumbai... 8 J B Nagar,Andheri East,Mumbai,Mumbai Suburban,... 9 Andheri East,Mumbai,Mumbai Suburban,Maharashtr... Name: political, dtype: object
将其转换为我们可以执行的方法
def get_cols(st):
pol = []
for i in result['address_components'].apply(json_normalize):
pol.append(','.join(i.apply(lambda x : x['long_name'] if st in x['types'] else np.nan,1).dropna()))
return pol
result['political'] = get_cols('political')
# This will assign the new column political with data.
答案 1 :(得分：1)
这是一个相当广泛的问题......
帮助您开始：
record_path = ['address_components']
meta= [
'formatted_address',
['geometry','location','lat'],
['geometry','location','lng'],
]
x = pd.io.json.json_normalize(d['results'], record_path, meta)
结果：
In [20]: pd.options.display.max_rows = 15
In [21]: x
Out[21]:
long_name short_name types \
0 Satya Niwas Satya Niwas [establishment, point_of_interest, premise]
1 Kanti Nagar Kanti Nagar [political, sublocality, sublocality_level_3]
2 J B Nagar J B Nagar [political, sublocality, sublocality_level_2]
3 Andheri East Andheri East [political, sublocality, sublocality_level_1]
4 Mumbai Mumbai [locality, political]
5 Mumbai Suburban Mumbai Suburban [administrative_area_level_2, political]
6 Maharashtra MH [administrative_area_level_1, political]
.. ... ... ...
83 India IN [country, political]
84 400047 400047 [postal_code]
85 Andheri East Andheri East [political, sublocality, sublocality_level_1]
86 Mumbai Mumbai [locality, political]
87 Mumbai Suburban Mumbai Suburban [administrative_area_level_2, political]
88 Maharashtra MH [administrative_area_level_1, political]
89 India IN [country, political]
formatted_address geometry.location.lat geometry.location.lng
0 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471
1 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471
2 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471
3 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471
4 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471
5 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471
6 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.109659 72.867471
.. ... ... ...
83 J B Nagar, Andheri East, Mumbai, Maharashtra 4... 19.111062 72.865592
84 J B Nagar, Andheri East, Mumbai, Maharashtra 4... 19.111062 72.865592
85 Andheri East, Mumbai, Maharashtra, India 19.115491 72.872695
86 Andheri East, Mumbai, Maharashtra, India 19.115491 72.872695
87 Andheri East, Mumbai, Maharashtra, India 19.115491 72.872695
88 Andheri East, Mumbai, Maharashtra, India 19.115491 72.872695
89 Andheri East, Mumbai, Maharashtra, India 19.115491 72.872695
[90 rows x 6 columns]
答案 2 :(得分：0)
您需要了解数据的架构。
json_st中的错误[＆＃39;结果＆＃39;] [＆＃39; address_components＆＃39;]
因为json_st [＆＃39;结果＆＃39;]是一个数组
在此处查看http://jsoneditoronline.org
以下是一些示例
for result in data['results']:
print type(result)
for address_component in result['address_components']:
print type(address_component)
print address_component['long_name']
print address_component['short_name']
for _type in address_component['types']:
print _type