JSON到pandas DataFrame

我试图做的是提取海拔数据从谷歌地图API沿纬度和经度坐标指定的路径，如下所示:

from urllib2 import Request, urlopen
import json

path1 = '42.974049,-81.205203|42.974298,-81.195755'
request=Request('http://maps.googleapis.com/maps/api/elevation/json?locations='+path1+'&sensor=false')
response = urlopen(request)
elevations = response.read()

得到的数据是这样的:

elevations.splitlines()

['{',
 '   "results" : [',
 '      {',
 '         "elevation" : 243.3462677001953,',
 '         "location" : {',
 '            "lat" : 42.974049,',
 '            "lng" : -81.205203',
 '         },',
 '         "resolution" : 19.08790397644043',
 '      },',
 '      {',
 '         "elevation" : 244.1318664550781,',
 '         "location" : {',
 '            "lat" : 42.974298,',
 '            "lng" : -81.19575500000001',
 '         },',
 '         "resolution" : 19.08790397644043',
 '      }',
 '   ],',
 '   "status" : "OK"',
 '}']

当放入作为DataFrame这里是我得到的:

pd.read_json(elevations)

这就是我想要的:

我不确定这是否可能，但主要是我在寻找的是一种方法，能够把海拔，纬度和经度数据放在一个熊猫数据框架(不需要有花哨的多行头)。

如果有人可以帮助或提供一些建议，这些数据的工作将是伟大的!如果你看不出我以前没有太多使用json数据…

编辑:

这个方法并不那么吸引人，但似乎很有效:

data = json.loads(elevations)
lat,lng,el = [],[],[]
for result in data['results']:
    lat.append(result[u'location'][u'lat'])
    lng.append(result[u'location'][u'lng'])
    el.append(result[u'elevation'])
df = pd.DataFrame([lat,lng,el]).T

最终数据框架有列纬度，经度，海拔

当前回答

问题是数据帧中有几列包含字典，其中包含更小的字典。有用的Json通常有大量嵌套。我一直在写一些小函数，把我想要的信息拉到一个新的列中。这样我就有了我想要的格式。

for row in range(len(data)):
    #First I load the dict (one at a time)
    n = data.loc[row,'dict_column']
    #Now I make a new column that pulls out the data that I want.
    data.loc[row,'new_column'] = n.get('key')

2014-10-20 04:54:03

其他回答

只是已接受答案的新版本，如python3。X不支持urllib2

from requests import request
import json
from pandas.io.json import json_normalize

path1 = '42.974049,-81.205203|42.974298,-81.195755'
response=request(url='http://maps.googleapis.com/maps/api/elevation/json?locations='+path1+'&sensor=false', method='get')
elevations = response.json()
elevations
data = json.loads(elevations)
json_normalize(data['results'])

2019-02-15 16:40:48

看看这个剪报。

# reading the JSON data using json.load()
file = 'data.json'
with open(file) as train_file:
    dict_train = json.load(train_file)

# converting json dataset from dictionary to dataframe
train = pd.DataFrame.from_dict(dict_train, orient='index')
train.reset_index(level=0, inplace=True)

希望能有所帮助。

2017-06-17 17:04:40

for row in range(len(data)):
    #First I load the dict (one at a time)
    n = data.loc[row,'dict_column']
    #Now I make a new column that pulls out the data that I want.
    data.loc[row,'new_column'] = n.get('key')

2014-10-20 04:54:03

一旦你有了被接受的答案所获得的扁平数据帧，你可以让列成为一个MultiIndex(“花式多行标题”)，就像这样:

df.columns = pd.MultiIndex.from_tuples([tuple(c.split('.')) for c in df.columns])

2019-06-16 15:20:48

优化已接受答案:

公认的答案有一些功能问题，所以我想分享我的代码，不依赖于urllib2:

import requests
from pandas import json_normalize
url = 'https://www.energidataservice.dk/proxy/api/datastore_search?resource_id=nordpoolmarket&limit=5'

response = requests.get(url)
dictr = response.json()
recs = dictr['result']['records']
df = json_normalize(recs)
print(df)

输出:

        _id                    HourUTC               HourDK  ... ElbasAveragePriceEUR  ElbasMaxPriceEUR  ElbasMinPriceEUR
0    264028  2019-01-01T00:00:00+00:00  2019-01-01T01:00:00  ...                  NaN               NaN               NaN
1    138428  2017-09-03T15:00:00+00:00  2017-09-03T17:00:00  ...                33.28              33.4              32.0
2    138429  2017-09-03T16:00:00+00:00  2017-09-03T18:00:00  ...                35.20              35.7              34.9
3    138430  2017-09-03T17:00:00+00:00  2017-09-03T19:00:00  ...                37.50              37.8              37.3
4    138431  2017-09-03T18:00:00+00:00  2017-09-03T20:00:00  ...                39.65              42.9              35.3
..      ...                        ...                  ...  ...                  ...               ...               ...
995  139290  2017-10-09T13:00:00+00:00  2017-10-09T15:00:00  ...                38.40              38.4              38.4
996  139291  2017-10-09T14:00:00+00:00  2017-10-09T16:00:00  ...                41.90              44.3              33.9
997  139292  2017-10-09T15:00:00+00:00  2017-10-09T17:00:00  ...                46.26              49.5              41.4
998  139293  2017-10-09T16:00:00+00:00  2017-10-09T18:00:00  ...                56.22              58.5              49.1
999  139294  2017-10-09T17:00:00+00:00  2017-10-09T19:00:00  ...                56.71              65.4              42.2

PS: API是指丹麦的电价

2020-01-06 19:26:34

JSON到pandas DataFrame

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