读取带有pd.read_JSON的JSON文件时出现ValueError错误

Question

我正在尝试使用pandas读取JSON文件：

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

我得到ValueError: arrays must all be same length

其他一些JSON页面显示此错误：

ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.

我该如何解读这些价值观呢？我对数据的有效性并不挑剔。

Answer 1

eht公司 在这种情况下，我们可以通过

import pandas as pd
df = pd.DataFrame(data["data"])

Answer 2

下面为我列出了密钥和值对：

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 3

看看json，它是有效的，但是它嵌套了数据和字段：

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']

您希望数据作为内容，字段作为列名：

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

有关更复杂的json数据帧提取，请参见^{}。