I found a quick and easy solution to what I wanted using json_normalize() included in pandas 1.01.

from urllib2 import Request, urlopen
import json

import pandas as pd    

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()
data = json.loads(elevations)
df = pd.json_normalize(data['results'])

This gives a nice flattened dataframe with the json data that I got from the Google Maps API.

In newer versions of pandas (0.20.0+, I believe), this can be done directly:

df.to_json('temp.json', orient='records', lines=True)

Direct compression is also possible:

df.to_json('temp.json.gz', orient='records', lines=True, compression='gzip')

you can use:

with open('json_example.json') as json_data:
    data = json.load(json_data)

df=pd.DataFrame.from_dict(data,orient='index').reset_index().rename(columns={'index':'Sentence',0:'Label'})

Creating dataframe from dictionary object.

import pandas as pd
data = [{'name': 'vikash', 'age': 27}, {'name': 'Satyam', 'age': 14}]
df = pd.DataFrame.from_dict(data, orient='columns')

df
Out[4]:
   age  name
0   27  vikash
1   14  Satyam

If you have nested columns then you first need to normalize the data:

data = [
  {
    'name': {
      'first': 'vikash',
      'last': 'singh'
    },
    'age': 27
  },
  {
    'name': {
      'first': 'satyam',
      'last': 'singh'
    },
    'age': 14
  }
]

df = pd.DataFrame.from_dict(pd.json_normalize(data), orient='columns')

df    
Out[8]:
age name.first  name.last
0   27  vikash  singh
1   14  satyam  singh

Source:

• pandas.DataFrame.from_dict
• pandas.json_normalize

From your code, it looks like you're loading a JSON file which has JSON data on each separate line. read_json supports a lines argument for data like this:

data_df = pd.read_json('C:/Users/Alberto/nutrients.json', lines=True)

Note
Remove lines=True if you have a single JSON object instead of individual JSON objects on each line.