python - 使用 ijson 在 Python 中解析 GoogleMaps API Json

Question

我正在尝试解析来自 GoogleMaps API 的 JSON 响应，以将嵌套字典中的值读取到 Pandas DataFrame 中。 JSON 响应的结构如下所示。

我正在尝试创建一个名为“address_components”的 DataFrame，其中(“types”、“short_name”、“long_name”)作为列标题，每个地址查询的相应值作为行。

我试过使用 ijson:

from ijson import items
import ijson
f = urlopen('https://maps.googleapis.com/maps/api/geocode/json?address=2022+Boren+Ave%2C+Seattle+98101')

objects = list(ijson.items(f, 'results.address_components'))

objects

输出:[]

我的问题是成功遍历嵌套的字典。

我还查看了 GoogleMaps GeoLocator API Documentation , 和 GoogleMaps JSON parsing docs . This solution seems to solve the problem, but on IOS

不帮我 Handlebars 伸进鸟巢...

    "results" : [
      {
         "address_components" : [
            {
               "long_name" : "1600",
               "short_name" : "1600",
               "types" : [ "street_number" ]
            },
            {
               "long_name" : "Amphitheatre Pkwy",
               "short_name" : "Amphitheatre Pkwy",
               "types" : [ "route" ]
            },
            {
               "long_name" : "Mountain View",
               "short_name" : "Mountain View",
               "types" : [ "locality", "political" ]
            },
            {
               "long_name" : "Santa Clara County",
               "short_name" : "Santa Clara County",
               "types" : [ "administrative_area_level_2", "political" ]
            },
            {
               "long_name" : "California",
               "short_name" : "CA",
               "types" : [ "administrative_area_level_1", "political" ]
            },
            {
               "long_name" : "United States",
               "short_name" : "US",
               "types" : [ "country", "political" ]
            },
            {
               "long_name" : "94043",
               "short_name" : "94043",
               "types" : [ "postal_code" ]
            }
         ],
         "formatted_address" : "1600 Amphitheatre Parkway, Mountain View, CA 94043, USA",
         "geometry" : {
            "location" : {
               "lat" : 37.4224764,
               "lng" : -122.0842499
            },
            "location_type" : "ROOFTOP",
            "viewport" : {
               "northeast" : {
                  "lat" : 37.4238253802915,
                  "lng" : -122.0829009197085
               },
               "southwest" : {
                  "lat" : 37.4211274197085,
                  "lng" : -122.0855988802915
               }
            }
         },
         "place_id" : "ChIJ2eUgeAK6j4ARbn5u_wAGqWA",
         "types" : [ "street_address" ]
      }
   ],
   "status" : "OK"
}

Answer 1

你可以使用json_normalize()为此::

import requests
import json
import pandas as pd

url = 'https://maps.googleapis.com/maps/api/geocode/json?address=2022+Boren+Ave%2C+Seattle+98101'

r = requests.get(url)

if r.status_code != requests.codes.ok:
    r.raise_for_status()

results = json.loads(r.text)

df = pd.io.json.json_normalize(results['results'], 'address_components')

print(df)

测试:

In [17]: pd.io.json.json_normalize(results['results'], 'address_components')
Out[17]:
       long_name   short_name                                     types
0           2022         2022                           [street_number]
1   Boren Avenue    Boren Ave                                   [route]
2       Belltown     Belltown                 [neighborhood, political]
3        Seattle      Seattle                     [locality, political]
4    King County  King County  [administrative_area_level_2, political]
5     Washington           WA  [administrative_area_level_1, political]
6  United States           US                      [country, political]
7          98121        98121                             [postal_code]