JSON to pandas DataFrame

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

from urllib2 import Request, urlopenimport jsonimport 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.

Check this snip out.

# 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 dataframetrain = pd.DataFrame.from_dict(dict_train, orient='index')train.reset_index(level=0, inplace=True)

Hope it helps :)

Optimization of the accepted answer:

The accepted answer has some functioning problems, so I want to share my code that does not rely on urllib2:

import requestsfrom pandas import json_normalizeurl = '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)

Output:

        _id                    HourUTC               HourDK  ... ElbasAveragePriceEUR  ElbasMaxPriceEUR  ElbasMinPriceEUR0    264028  2019-01-01T00:00:00+00:00  2019-01-01T01:00:00  ...                  NaN               NaN               NaN1    138428  2017-09-03T15:00:00+00:00  2017-09-03T17:00:00  ...                33.28              33.4              32.02    138429  2017-09-03T16:00:00+00:00  2017-09-03T18:00:00  ...                35.20              35.7              34.93    138430  2017-09-03T17:00:00+00:00  2017-09-03T19:00:00  ...                37.50              37.8              37.34    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              38.4996  139291  2017-10-09T14:00:00+00:00  2017-10-09T16:00:00  ...                41.90              44.3              33.9997  139292  2017-10-09T15:00:00+00:00  2017-10-09T17:00:00  ...                46.26              49.5              41.4998  139293  2017-10-09T16:00:00+00:00  2017-10-09T18:00:00  ...                56.22              58.5              49.1999  139294  2017-10-09T17:00:00+00:00  2017-10-09T19:00:00  ...                56.71              65.4              42.2 

PS: API is for Danish electricity prices