ValueError errors while reading JSON file with pd.read_json

Question

I am trying to read JSON file using pandas:

import pandas as pd
df = pd.read_json(\'https://data.gov.in/node/305681/datastore/export/json\')

I

Answer 1

The following listed both the key and value pair for me:

from urllib.request import urlopen
import json 
from pandas.io.json import json_normalize
import pandas as pd
import requests

df = json.loads(requests.get('https://api.github.com/repos/akkhil2012/MachineLearning').text)

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

print(data)

Answer 2

Looking at the json it is valid, but it's nested with data and fields:

import json
import requests

In [11]: d = json.loads(requests.get('https://data.gov.in/node/305681/datastore/export/json').text)

In [12]: list(d.keys())
Out[12]: ['data', 'fields']

You want the data as the content, and fields as the column names:

In [13]: pd.DataFrame(d["data"], columns=[x["label"] for x in d["fields"]])
Out[13]:
   S. No.                   States/UTs    2008-09    2009-10    2010-11    2011-12    2012-13
0       1               Andhra Pradesh  183446.36  193958.45  201277.09  212103.27  222973.83
1       2            Arunachal Pradesh      360.5     380.15     407.42        419     438.69
2       3                        Assam    4658.93    4671.22    4707.31       4705    4709.58
3       4                        Bihar   10740.43   11001.77    7446.08       7552    8371.86
4       5                 Chhattisgarh    9737.92   10520.01   12454.34   12984.44   13704.06
5       6                          Goa     148.61        148        149     149.45     457.87
6       7                      Gujarat   12675.35   12761.98   13269.23   14269.19   14558.39
7       8                      Haryana   38149.81   38453.06   39644.17   41141.91   42342.66
8       9             Himachal Pradesh      977.3    1000.26    1020.62    1049.66    1069.39
9      10            Jammu and Kashmir    7208.26    7242.01    7725.19     6519.8    6715.41
10     11                    Jharkhand    3994.77    3924.73    4153.16    4313.22    4238.95
11     12                    Karnataka   23687.61    29094.3   30674.18   34698.77   36773.33
12     13                       Kerala   15094.54   16329.52   16856.02   17048.89   22375.28
13     14               Madhya Pradesh     6712.6    7075.48    7577.23    7971.53    8710.78
14     15                  Maharashtra   35502.28   38640.12    42245.1   43860.99   45661.07
15     16                      Manipur    1105.25       1119    1137.05    1149.17    1162.19
16     17                    Meghalaya     994.52     999.47    1010.77    1021.14    1028.18
17     18                      Mizoram     411.14     370.92     387.32     349.33     352.02
18     19                     Nagaland     831.92      833.5     802.03     703.65     617.98
19     20                       Odisha   19940.15   23193.01   23570.78   23006.87   23229.84
20     21                       Punjab    36789.7   32828.13   35449.01      36030   37911.01
21     22                    Rajasthan    6449.17    6713.38    6696.92    9605.43    10334.9
22     23                       Sikkim     136.51     136.07     139.83     146.24        146
23     24                   Tamil Nadu   88097.59  108475.73  115137.14  118518.45  119333.55
24     25                      Tripura    1388.41    1442.39    1569.45       1650    1565.17
25     26                Uttar Pradesh    10139.8   10596.17   10990.72   16075.42   17073.67
26     27                  Uttarakhand    1961.81    2535.77    2613.81    2711.96    3079.14
27     28                  West Bengal    33055.7   36977.96   39939.32   43432.71   47114.91
28     29  Andaman and Nicobar Islands     617.58     657.44     671.78        780     741.32
29     30                   Chandigarh     272.88     248.53     180.06     180.56     170.27
30     31       Dadra and Nagar Haveli      70.66      70.71      70.28         73         73
31     32                Daman and Diu      18.83       18.9      18.81      19.67         20
32     33                        Delhi       1.17       1.17       1.17       1.23         NA
33     34                  Lakshadweep     134.64     138.22     137.98     139.86     139.99
34     35                   Puducherry     111.69     112.84     113.53        116     112.89

See also json_normalize for more complex json DataFrame extraction.