Python Extract Elements From Json String
I have a Json string from which I'm able to extract few components like formatted_address,lat,lng, but I'm unable to extract feature(values) of other components like intersection,
Solution 1:
I would go for json_normalize, thought of one line answer but I dont think its possible i.e (Here I did only for px_val and py_val you can do similar things for other columns)
from pandas.io.json import json_normalize
import pandas as pd
import json
withopen('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
I try to parse political certainly not proud of this solution i.e
pol = []
for i inresult['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
Output 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
To convert it to a method we can do
defget_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. Solution 2:
This is rather a pretty broad question...
To help you to start:
record_path = ['address_components']
meta= [
'formatted_address',
['geometry','location','lat'],
['geometry','location','lng'],
]
x = pd.io.json.json_normalize(d['results'], record_path, meta)
Result:
In [20]: pd.options.display.max_rows =15In [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]
84400047400047 [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... 19.10965972.8674711 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.10965972.8674712 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.10965972.8674713 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.10965972.8674714 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.10965972.8674715 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.10965972.8674716 Satya Niwas, Kanti Nagar, J B Nagar, Andheri E... 19.10965972.867471
.. ... ... ...
83 J B Nagar, Andheri East, Mumbai, Maharashtra 4... 19.11106272.86559284 J B Nagar, Andheri East, Mumbai, Maharashtra 4... 19.11106272.86559285 Andheri East, Mumbai, Maharashtra, India 19.11549172.87269586 Andheri East, Mumbai, Maharashtra, India 19.11549172.87269587 Andheri East, Mumbai, Maharashtra, India 19.11549172.87269588 Andheri East, Mumbai, Maharashtra, India 19.11549172.87269589 Andheri East, Mumbai, Maharashtra, India 19.11549172.872695
[90rows x 6 columns]
Solution 3:
You need to understand schema of your data.
Error in json_st['results']['address_components']
because json_st['results'] is an array
Check it here http://jsoneditoronline.org
Here is some sample
for result in data['results']:
printtype(result)
for address_component in result['address_components']:
printtype(address_component)
print address_component['long_name']
print address_component['short_name']
for _typein address_component['types']:
print _type
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